The Journal of China Universities of Posts and Telecommunications, 2012
Bag-of-words (BoW) representation becomes one of the most popular methods for representing image ... more Bag-of-words (BoW) representation becomes one of the most popular methods for representing image content and has been successfully applied to object categorization. This paper uses the newly proposed statistics of word activation forces (WAFs) to reduce the redundancy in the codebook used in the BoW model. In such a way, the representation of image features is improved. In addition, the authors propose a method using soft inverse document frequency (Soft-IDF) to optimize BoW based image features. Given visual words and the dataset, each visual word appears in different amount of images and also different times in each particular image. Some of the visual words appear rare in contrary to the frequent ones. The proposed method balances this case. Experiments show encouraging results in scene categorization by the proposed approach.
Electronic Journal of Differential Equations, 2006
Let Λ p p be the best Sobolev embedding constant of W 1,p (Ω) → L p (∂Ω), where Ω is a smooth bou... more Let Λ p p be the best Sobolev embedding constant of W 1,p (Ω) → L p (∂Ω), where Ω is a smooth bounded domain in R N. We prove that as p → ∞ the sequence Λp converges to a constant independent of the shape and the volume of Ω, namely 1. Moreover, for any sequence of eigenfunctions up (associated with Λp), normalized by up L ∞ (∂Ω) = 1, there is a subsequence converging to a limit function u∞ which satisfies, in the viscosity sense, an ∞-Laplacian equation with a boundary condition.
IEICE Transactions on Information and Systems, 2014
A discriminative reference-based method for scene image categorization is presented in this lette... more A discriminative reference-based method for scene image categorization is presented in this letter. Reference-based image classification approach combined with K-SVD is approved to be a simple, efficient, and effective method for scene image categorization. It learns a subspace as a means of randomly selecting a reference-set and uses it to represent images. A good reference-set should be both representative and discriminative. More specifically, the reference-set subspace should well span the data space while maintaining low redundancy. To automatically select reference images, we adapt affinity propagation algorithm based on data similarity to gather a reference-set that is both representative and discriminative. We apply the discriminative reference-based method to the task of scene categorization on some benchmark datasets. Extensive experiment results demonstrate that the proposed scene categorization method with selected reference set achieves better performance and higher efficiency compared to the state-of-the-art methods.
The Journal of China Universities of Posts and Telecommunications, 2012
Bag-of-words (BoW) representation becomes one of the most popular methods for representing image ... more Bag-of-words (BoW) representation becomes one of the most popular methods for representing image content and has been successfully applied to object categorization. This paper uses the newly proposed statistics of word activation forces (WAFs) to reduce the redundancy in the codebook used in the BoW model. In such a way, the representation of image features is improved. In addition, the authors propose a method using soft inverse document frequency (Soft-IDF) to optimize BoW based image features. Given visual words and the dataset, each visual word appears in different amount of images and also different times in each particular image. Some of the visual words appear rare in contrary to the frequent ones. The proposed method balances this case. Experiments show encouraging results in scene categorization by the proposed approach.
Electronic Journal of Differential Equations, 2006
Let Λ p p be the best Sobolev embedding constant of W 1,p (Ω) → L p (∂Ω), where Ω is a smooth bou... more Let Λ p p be the best Sobolev embedding constant of W 1,p (Ω) → L p (∂Ω), where Ω is a smooth bounded domain in R N. We prove that as p → ∞ the sequence Λp converges to a constant independent of the shape and the volume of Ω, namely 1. Moreover, for any sequence of eigenfunctions up (associated with Λp), normalized by up L ∞ (∂Ω) = 1, there is a subsequence converging to a limit function u∞ which satisfies, in the viscosity sense, an ∞-Laplacian equation with a boundary condition.
IEICE Transactions on Information and Systems, 2014
A discriminative reference-based method for scene image categorization is presented in this lette... more A discriminative reference-based method for scene image categorization is presented in this letter. Reference-based image classification approach combined with K-SVD is approved to be a simple, efficient, and effective method for scene image categorization. It learns a subspace as a means of randomly selecting a reference-set and uses it to represent images. A good reference-set should be both representative and discriminative. More specifically, the reference-set subspace should well span the data space while maintaining low redundancy. To automatically select reference images, we adapt affinity propagation algorithm based on data similarity to gather a reference-set that is both representative and discriminative. We apply the discriminative reference-based method to the task of scene categorization on some benchmark datasets. Extensive experiment results demonstrate that the proposed scene categorization method with selected reference set achieves better performance and higher efficiency compared to the state-of-the-art methods.
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