Color Histogram

A fundamental aspect of content-based image retrieval (CBIR) is the extraction and the representation of a visual feature that is an effective discriminant between pairs of images. Among the many visual features that have been studied, the distribution of color pixels in an image is the most common visual feature studied. The standard representation of color for content-based indexing in image databases is the color histogram. Vector-based distance functions are used to compute the similarity between two images as the distance between points in the color histogram space. This paper proposes an alternative real valued representation of color based on the information theoretic concept of entropy. A theoretical presentation of image entropy is accompanied by a practical description of the merits and limitations of image entropy compared to color histograms. Specifically, the L 1 norm for color histograms is shown to provide an upper bound on the difference between image entropy values. Our initial results suggest that image entropy is a promising approach to image description and representation.

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The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. This paper presents the content based image retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image Retrieval).The texture and color features are extracted through wavelet transformation and color histogram and the combination of these features is robust to scaling and translation of objects in an image. The proposed system has demonstrated a promising and faster retrieval method on a WANG image database containing 1000 general-purpose color images. The performance has been evaluated by comparing with the existing systems in the literature.

The conventional histogram intersection (HI) algorithm computes the intersected section of the corresponding color histograms in order to measure the matching rate between two color images. Since this algorithm is strictly based on the matching between bins of identical colors, the final matching rate can be easily affected by color variation caused by various environment changes. In this paper, a Gaussian weighted histogram intersection (GWHI) algorithm is proposed to facilitate the histogram matching via taking into account matching of both identical and similar colors. The weight is determined by the distance between two colors. The algorithm is applied to license plate classification. Experimental results show that the proposed algorithm produces a much lower intra-class distance and a much higher inter-class distance than previous HI algorithms for tested images which are captured under various illumination conditions.

A fast and simple method for content based retrieval using the DC-pictures of H.264 coded video without full decompression is presented. Compressed domain retrieval is very desirable for content analysis and retrieval of compressed image and video. Even though, DCpictures are among the most widely used compressed domain indexing and retrieval methods in pre H.264 coded videos, they are not generally used in the H.264 coded video. This is due to two main facts, first, the I-Frame in the H.264 standard are spatially predicatively coded and second, the H.264 standard employs Integer Discrete Cosine Transform. In this paper we have applied color histogram indexing method on the DC-pictures derived from H.264 coded I-frames. Since the method is based on independent I-frame coded pictures, it can be used either for video analysis of H.264 coded videos, or image retrieval of the I-frame based coded images such as advanced image coding. The retrieval performance of the proposed algorithm is compared with that the fully decoded images. Simulation results indicate that the performance of the proposed method is very close to the fully decompressed image systems. Moreover the proposed method has much lower computational load.

Content-based image retrieval (CBIR) is a collection of techniques for retrieving images on the basis of features, such as color, texture and shape. An efficient tool, which is widely used in CBIR, is that of color image histograms. The classic method of color histogram creation results in very large histograms with large variations between neighboring bins. Thus, small changes in the image might result in great changes in the histogram. Moreover, the fact that each color space consists of three components leads to 3-dimensional histograms. Manipulating and comparing 3D histograms is a complicated and computationally expensive procedure. The need, therefore, for reduction of the three dimensions to one could lead to efficient approaches. This procedure of projecting the 3D histogram onto one single-dimension histogram is called histogram linking. In this paper, a new fuzzy linking method of color histogram creation is proposed based on the L*a*b* color space and provides a histogram which contains only 10 bins. The histogram creation method in hand was assessed based on the performances achieved in retrieving similar images from a widely diverse image collection. The experimental results prove that the proposed method is less sensitive to various changes in the images (such as lighting variations, occlusions and noise) than other methods of histogram creation.

Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level cooccurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is concluded that, for the database images used in this work, the CBIR using hybrid technique is better for image retrieval because it has a higher match performance (100%) for each type of similarity measure so; it is the best one for image retrieval.

Current web video search results rely exclusively on text keywords or user-supplied tags. A search on typical popular video often returns many duplicate and near-duplicate videos in the top results. This paper outlines ways to cluster and filter out the nearduplicate video using a hierarchical approach. Initial triage is performed using fast signatures derived from color histograms. Only when a video cannot be clearly classified as novel or nearduplicate using global signatures, we apply a more expensive local feature based near-duplicate detection which provides very accurate duplicate analysis through more costly computation. The results of 24 queries in a data set of 12,790 videos retrieved from Google, Yahoo! and YouTube show that this hierarchical approach can dramatically reduce redundant video displayed to the user in the top result set, at relatively small computational cost.

The probabilistic visual tracking methods using color histograms have been proven to be robust to target model variations and background illumination changes as shown by the recent research. However, the required computational cost is high due to intensive image data processing. The embedded solution of such algorithms become challenging due to high computational power demand and algorithm complexity. This paper presents a hardware/software co-design architecture for implementation of the wellknown kernel based mean shift tracking algorithm. The design uses color histogram of the target as tracking feature. The target is searched in the consecutive images by maximizing the statistical match of the color distributions. The target localization is based on gradient based iterative search instead of exhaustive search which makes the system capable of achieving frame rate up to hundreds of frames per second while tracking multiple targets. The design, which is fully standalone, is implemented on a low-cost medium-size field programmable gate array (FPGA) device. The hardware cost of the design is compared with some other tracking systems. The performance of the system in terms of speed is evaluated and compared with the software based implementation. It is expected that the proposed solution will find its utility in applications like embedded automatic video surveillance systems.

We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360 0 videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.

A novel color image histogram equalization approach is proposed that exploits the correlation between color components and it is enhanced by a multi-level smoothing technique borrowed from statistical language engineering. Multi-level smoothing aims at dealing efficiently with the problem of unseen color values, either considered independently or in combination with others. It is applied here to the HSI color space for the probability of intensity and the probability of saturation given the intensity, while the hue is left unchanged. Moreover, the proposed approach is extended by an empirical technique, which is based on a hue preserving non-linear transformation, in order to eliminate the gamut problem. This is the second method proposed in the paper. The equalized images by the two methods are compared to those produced by other well-known methods. The better quality of the images equalized by the proposed methods is judged in terms of their visual appeal and objective figures of merit, such as the entropy and the Kullback–Leibler divergence estimates between the resulting color histogram and the multivariate uniform probability density function.

People Tracking in multiple cameras is of great interest for wide area video surveillance systems. Multi-camera tracking with non-overlapping fields of view (FOV) involves the tracking of people in the blind region and their correspondence matching across cameras. We consider these problems in this paper. We propose a multi camera architecture for wide area surveillance and a real time people tracking algorithm across non overlapping cameras. We compared different methods to evaluate the color Brightness Transfer Function (BTF) between non overlapping cameras. These approaches are based on a testing phase during which the color histogram mapping, between pairs of images of the same object observed in the different field of views, is carried out. The experimental results compare two different transfer functions and demonstrate their limits in people association when a new person enters in one camera FOV.

The aim of this paper is to present a comparative study of two linear dimension reduction methods namely PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main idea of PCA is to transform the high dimensional input space onto the feature space where the maximal variance is displayed. The feature selection in traditional LDA is obtained by maximizing the difference between classes and minimizing the distance within classes. PCA finds the axes with maximum variance for the whole data set where LDA tries to find the axes for best class seperability. The proposed method is experimented over a general image database
using Matlab. The performance of these systems has been evaluated by Precision and Recall measures. Experimental results show that PCA based dimension reduction method gives the better performance in terms of higher precision and recall values with lesser computational complexity than the LDA based method.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

In a content-based image retrieval system (CBIR), the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity
measures based on which matches are made and images are retrieved are also discussed. The paper discusses effective indexing and fast searching of images based on visual features.

The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. This paper presents the content based image retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image Retrieval).The texture and color features are extracted through wavelet transformation and color histogram and the combination of these features is robust to scaling and translation of objects in an image. The proposed system has demonstrated a promising and faster retrieval method on a WANG image database containing 1000 general-purpose color images. The performance has been evaluated by comparing with the existing systems in the literature.

A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation-maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.

We present a method designed to solve the problem of automatic color shade grading for industrial inspection of randomly textured multicolored ceramic tiles. Our grading method is based on the comparison of color histograms and we present results of correctly grading several series of ceramic tiles, the differences of which were at the threshold of human perception.

This paper presents a method for the extraction of contour lines and other geographic information from scanned color images of topographical maps. Although topographic maps are available from many suppliers, this work focuses on United States Geological Survey (USGS) maps. The extraction of contour lines, which are shown with brown color on USGS maps, is a difficult process due to aliasing and false colors induced by the scanning process and due to closely spaced and intersecting/overlapping features inherent to the map. These difficulties render simple approaches such as clustering ineffective. The proposed method overcomes these difficulties using a multistep process. First, a color key set, designed to comprehend color aliasing and false colors, is generated using an eigenvector line-fitting technique in RGB space. Next, area features, representing vegetation and bodies of water, are extracted using RGB color histogram analysis in order to simplify the next stage. Then, linear features corresponding to roads and rivers including contours, are extracted using a valley seeking algorithm operating on a transformed version of the original map. Finally, an A* search algorithm is used to link valleys together to form linear features and to close the gaps caused by intersecting features. The performance of the algorithm is tested on a number of USGS topographic map samples.

Bhattacharyya coefficient is a popular method that uses color histograms to correlate images. Bhattacharyya Coefficient is believed to be the absolute similarity measure for frequency coded data and it needs no bias correction. In this paper, we show that when this method is applied to gray scale images, it produces biased results. Correlation based on this measure is not adequate for common gray scale images, as the color in grayscale is not a sufficient feature. The b iased ness is explored and demonstrated through numerous experiments with different kinds of non-rigid maneuvering objects in cluttered and less cluttered environments, in context to the object tracking. The spectral performance of the Bhattacharyya curve is compared with the spatial matching criterion i.e. Mean Square Difference.

Koleksi-koleksi gambar digital di bidang perdagangan, pemerintahan, akademik, dan rumah sakit jumlahnya semakin banyak. Koleksi tersebut merupakan hasil digitalisasi foto-foto analog, diagram-diagram, lukisan-lukisan, gambar-gambar, dan buku-buku. Cara yang biasa dipakai untuk mencari koleksi tersebut adalah menggunakan metadata, seperti tag, caption, atau keywords. Tentu saja cara ini tidak praktis, melelahkan, dan juga mahal karena masih menggunakan tenaga manusia untuk mendeskripsikan gambar dalam database. Tulisan ini membahas informasi umum tentang penggunaan histogram warna (color histogram), dalam pencarian gambar (image retrieval).

Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. Color and texture features are important properties in content-based image retrieval systems. We use color histogram, color moments, and Color Coherence Vector (CCV) features as color descriptors and co-occurrence matrices and discrete wavelet transform features as texture descriptors in this paper. Both color and texture features of images are extracted and stored as feature vectors in a database. During the retrieval process, the color and texture feature vector of the query image is computed and matched against those features in the database. We use sum-of-squared-differences for similarity measurement. The goal of this paper is to determine which color or texture features are the most efficient to represent similarity of color images. Our initial results show that the color moment and color histogram descriptors are not effective features because they do not consider spatial information of image pixels. Therefore, different images may have similar color distributions. In addition, our results show that the CCV and co-occurrence matrix features retrieve much more relevant images than other color and texture features. Additionally, in order to increase precision, the combination of color and texture features should be used in CBIR systems.

This paper describes the development of a project based on Fuzzy Logic applied to color histograms for color grading wood boards. During this development a fuzzy min-max inference system, with bell membership functions adjusted to the histogram, was implemented and evaluated. This system performs image matching. Images given as training samples are used to define the classes that determine the several wood board color intervals. In that way the system is capable to classify an image by its color in order to group wood boards with the same tonalitycolor in a short period of time.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, effective indices can be built for an image database. In this paper, we cast the image classification problem in a Bayesian framework. Specifically, we consider city vs. landscape classification and further classification of landscape images into sunset, forest, and mountain classes. We demonstrate how high-level concepts can be understood from specific low-level image features under the constraint that the test images do belong to one of the classes in concern. We further demonstrate that a small codebook (the optimal size of codebook is selected using MDL principle) extracted from a vector quantizer can be used to estimate the class-conditional densities needed for the Bayesian methodology. Classification based on color histograms, color coherence vectors, edge direction histograms, and edge direction coherence vectors as features shows promising results. On a database of

Specular reflections present difficulties for many areas of computer vision such as stereo and segmentation. To separate specular and diffuse reflection components, previous approaches generally require accurate segmentation, regionally uniform reflectance or structured lighting. To overcome these limiting assumptions, we propose a method based on color analysis and multibaseline stereo that simultaneously estimates the separation and the true depth of specular reflections. First, pixels with a specular component are detected by a novel form of color histogram differencing that utilizes the epipolar constraint. This process uses relevant data from all the stereo images for robustness, and addresses the problem of color occlusions. Based on the Lambertian model of diffuse reflectance, stereo correspondence is then employed to compute for specular pixels their corresponding diffuse components in other views. The results of color-based detection aid the stereo correspondence, which determines both separation and true depth of specular pixels. Our approach integrates color analysis and multibaseline stereo in a synergistic manner to yield accurate separation and depth, as demonstrated by our results on synthetic and real image sequences. † This work was performed while the second author was visiting Microsoft Research, Asia.

In this paper we present a statistical learning scheme for image classification based on a mixture of old fashioned ideas and state of the art learning tools. We represent input images through large dimensional and usually sparse histograms which, depending on the task, are either color histograms or co-occurrence matrices. Support Vector Machines are trained on these sparse inputs directly to solve problems like indoor/outdoor classification and cityscape retrieval from image databases. The experimental results indicate that the use of a kernel function derived from the computer vision literature leads to better recognition results than off the shelf kernels. According to our findings, it appears that image classification problems can be addressed with no need of explicit feature extraction or dimensionality reduction stages. We argue that this might be used as the starting point for developing image classification systems which can be easily tuned to a number of different tasks.

This paper proposes a new image representation scheme for database indexing. The application domain is \image-based" indexing as opposed to \content-based" indexing. In other words, given a database of images and associated data, we w ant to search the database for a known image so as to retrieve the data associated with the image. A concrete example is to search a database of aerial images so as to retrieve the GPS coordinates of a known scene. Our representation is based on a hierarchy of color histograms and o ers a compromise between methods based on a global histogram and methods based on multiple histograms corresponding to segmented regions. Our method retains positional information better than the above schemes and naturally leads to a multi-level comparison strategy where mismatches are quickly discarded at higher levels. Our method o ers a simple, easily implementable solution to the speci c problem of image-based indexing.

In this paper, we propose a novel query language for video indexing and retrieval that (1) enables to make queries both at the image level and at the semantic level (2) enables the users to define their own scenarios based on semantic events and (3) retrieves videos with both exact matching and similarity matching. For a query language, four main issues must be addressed: data modeling, query formulation, query parsing and query matching. In this paper we focus and give contributions on data modeling, query formulation and query matching. We are currently using color histograms and SIFT features at the image level and 10 types of events at the semantic level. We have tested the proposed query language for the retrieval of surveillance videos of a metro station. In our experiments the database contains more than 200 indexed physical objects and 48 semantic events. The results using different types of queries are promising.

Object tracking in real time videos is a critical task .So far many algorithms have been proposed to overcome the difficulties which arises from noise, occlusions and changes in the foreground object. But still there is a need for an efficient algorithm that can effectively track the object due to its varying position under various ambient conditions. Among various tracking methods, the mean shift tracking algorithm is a most widely used one due to its efficiency and simplicity. The experimental results show that the method which has been proposed can effectively track an object under the condition of varying illumination and shape deformation. Here the Correlation factor is also used to check the effective working of Mean shift algorithm and to make comparison between RGB and LBP technique.

This paper presents a location-sensitive algorithm for the automated insertion of virtual advertisements in videos. Virtual advertisements are objects (text messages, pictures, an animation or video sequence) that are introduced noninvasively into a video. Typically, this is done manually. Most automated approaches involve insertion of virtual advertisements in hardcoded, specific locations of videos that could be intrusive. In this paper we propose an algorithm based on colour histogram processing that works with an inter-frame difference based classifier. Along with the spatial localization input (based on the type of video), this ensures a virtual advertisement is inserted in a manner that it is noticed and yet appears at a location that does not obfuscate a critical portion of the video. We demonstrate the efficacy of this method through experimental results.

This paper describes an approach to training a database of building images under the supervision of a user. Then it will be applied to recognize buildings in an urban scene. Given a set of training images, we first detect the building facets and calculate their properties such as area, wall color histogram and a list of local features. All facets

Images or videos may be imaged under di erent illuminants than models in an image or video proxy database. Changing illumination color in particular may confound recognition algorithms based on color histograms or video segmentation routines based on these. Here we show that a very simple method of discounting illumination changes is adequate for both image retrieval and video segmentation tasks. We develop a feature vector of only 36 values that can also be em used for both these objectives as well as for retrieval of video proxy images from a database. The new image metric is based on a color channel normalization step, followed by reduction of dimensionality b y going to a chromaticity space. Treating chromaticity histograms as images, w e perform an e ective l o w pass ltering of the histogram by rst reducing its resolution via a wavelet based compression and then by a DCT transformation followed by zonal coding. We show that the color constancy step | color band normalization | can be carried out in the compressed domain for images that are stored in compressed form, and that only a small amount of image information need be decompressed in order to calculate the new metric. The new method performs better than previous methods tested for image or texture recognition and operates entirely in the compressed domain, on feature vectors. As well as achieving illumination invariance for video segmentation, so that e.g. an actor stepping out of a shadow does not trigger the declaration of a false cut, the metric reduces all videos to a uniform scale. Thus thresholds can be developed for a training set of videos and applied to any new video, including streaming video, for segmentation as a one pass operation.

In this paper a new cut detection algorithm, adapted to the segmentation of animation movies, is proposed. A cut is a direct concatenation of two different shots (fundamental video units) that produces a temporal visual discontinuity in the video stream. As color is a major feature of animation movies (each movie has its own particular color distribution) the proposed algorithm uses second order derivatives and Euclidean distances between color histograms of frames in order to detect the cuts. For frame classification, an automatic threshold estimation is proposed. Also, in order to reduce false detections, we propose an algorithm to detect an effect specific to animation movies, named "short color change" (i.e. thunders, lightening). The resulting method achieved better results compared to the classical histogram-based and motiondiscontinuity based approaches, as shown by tests conducted on several animation movies.

We present a linear algorithm for the computation of the illuminant change occurring between two color pictures of a scene. We model the light variations with the von Kries diagonal transform and we estimate it by minimizing a dissimilarity measure between the piecewise inversions of the cumulative color histograms of the considered images. We also propose a method for illuminant invariant image recognition based on our von Kries transform estimate.

The performance of Content-Based Image Retrieval (CBIR) system is depends on efficient feature extraction and accurate retrieval of similar images. Content based image retrieval is the task of retrieving the images from the large collection of database on the basis of their own visual content. This paper combines color features using Color Descriptor (CN) and texture features using Gray Level Co-occurrence Matrix (GLCM) to obtain better retrieval efficiency.From large database,using these feature vectors near about similarly matched images are retrieved. It is observed that combination of color features with the texture provides approximately similar results with very less processing time if we compare with individual approach.

A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation-maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.

Integrative Co-occurrence matrices are introduced as novel features for color texture classiÿcation. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information proÿt of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classiÿcation experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classiÿcation results up to 20% and 32% for the ÿrst and second baseline, respectively.

I. INTRODUCTION We propose a content-based image retrieval system, using color features, in this work. Digital image retrieval, an important sub-domain of multimedia (information) retrieval [1], is digital image retrieval [2]. An image retrieval system is a ...

Color analysis is frequently used in image/video retrieval. However, many existing color spaces and color distances fail to correctly capture color differences usually perceived by the human eye. The objective of this paper is to first highlight the limitations of existing color spaces and similarity measures in representing human perception of colors, and then to propose (i) a new perceptual color space model called HCL, and (ii) an associated color similarity measure denoted DHCL. Experimental results show that using DHCL on the new color space HCL leads to a solution very close to human perception of colors and hence to a potentially more effective content-based image/video retrieval. Moreover, the application of the similarity measure DHCL to other spaces like HSV leads to a better retrieval effectiveness. A comparison of HCL against L*C*H and CIECAM02 spaces using color histograms and a similarity distance based on Dirichlet distribution illustrates the good performance of HCL for a collection of 3500 images of different kinds.

Koleksi-koleksi gambar digital di bidang perdagangan, pemerintahan, akademik, dan rumah sakit jumlahnya semakin banyak. Koleksi tersebut merupakan hasil digitalisasi foto-foto analog, diagram-diagram, lukisan-lukisan, gambar-gambar, dan buku-buku. Cara yang biasa dipakai untuk mencari koleksi tersebut adalah menggunakan metadata, seperti tag, caption, atau keywords. Tentu saja cara ini tidak praktis, melelahkan, dan juga mahal karena masih menggunakan tenaga manusia untuk mendeskripsikan gambar dalam database. Tulisan ini membahas informasi umum tentang penggunaan histogram warna (color histogram), dalam pencarian gambar (image retrieval).

Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. The color particle filter has proven to be an efficient, simple and robust tracking algorithm. In this paper, we present a hybrid valued sequential state estimation algorithm, and its particle filter-based implementation, that extends the standard color particle filter in two ways. Firstly, target detection and deletion are embedded in the particle filter without relying on an external track initialization and cancellation algorithm. Secondly, the algorithm is able to track multiple objects sharing the same color description while keeping the attractive properties of the original color particle filter. The performance of the proposed filter are evaluated qualitatively on various real-world video sequences with appearing and disappearing targets.

... have experimented with different color spaces (eg, RGB, CIE-Lab), various color descriptors (eg, color ... overcome the above limitation, an attempt was made recently [14] to combine the benefits ... images (A) and dissimilar images (B), based on the k = 15 color prototypes shown in ...

This article provides a color-based image retrieval technique for RGB image databases. Our proposed CBIR system uses the query by example approach and a relevance feedback mechanism. Feature extraction process is performed by computing a global color histogram for each image. Feature vectors are compared using the histogram intersection difference metric, and a relevance feedback mechanism is used in the retrieval process.