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locality-constrained linear coding Based on Principal Components of Visual Vocabulary for Visual Object Categorization 14

Locality-constrained Linear Coding Based on Principal Compon...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Wang, Hongxia Zeng, Long Peng, Dewei Geng, Feng Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781467365932

Through the linear correlation analysis between the local feature and its K-nearest-neighbor visual words and significance testing of locality-constrained linear coding, this paper finds that the fundamental reason for causing nonsignificance of the weight coefficient is the multicollinearity of K-nearest-neighbor visual words in locality-constrained linear coding (LLC) scheme. locality-constrained principal component linear coding can solve the multicollinearity and improves the classification accuracy, but it increases the time overhead of the coding. This paper presents an improved scheme called locality-constrained linear coding based on the principal components of visual vocabulary. To determine the principal components of K-nearest-neighbor visual words of each local feature is simplified to only determine the principal components of visual vocabulary. Experiments have been conducted for comparing and evaluating the proposed method utilizing the Caltech-4 dataset. Experimental results show that locality-constrained linear coding based on the principal components of visual vocabulary reduces the time overhead and the same time it retains the advantages of locality-constrained principal component linear coding.

关键词： visual object categorization local feature linear coding principal components

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locality-constrained linear coding Based on Principal Components of Visual Vocabulary for Visual Object Categorization

Locality-constrained Linear Coding Based on Principal Compon...

引用

The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

作者： Hongxia Wang Long Zeng Dewei Peng Feng Geng School of Computer Science & Technology Wuhan University of Technology Wuhan HubeiChina

Through the linear correlation analysis between the local feature and its K-nearest-neighbor visual words and significance testing of locality-constrained linear coding,this paper finds that the fundamental reason for causing nonsignificance of the weight coefficient is the multicollinearity of K-nearest-neighbor visual words in locality-constrained linear coding(LLC)***-constrained principal component linear coding can solve the multicollinearity and improves the classification accuracy,but it increases the time overhead of the *** paper presents an improved scheme called locality-constrained linear coding based on the principal components of visual *** determine the principal components of K-nearest-neighbor visual words of each local feature is simplified to only determine the principal components of visual *** have been conducted for comparing and evaluating the proposed method utilizing the Caltech-4 *** results show that locality-constrained linear coding based on the principal components of visual vocabulary reduces the time overhead and the same time it retains the advantages of localityconstrained principal component linear coding.

关键词： visual object categorization local feature linear coding principal components

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locality-constrained linear coding Based on Principal Components of Visual Vocabulary for Visual Object Categorization

Locality-constrained Linear Coding Based on Principal Compon...

引用

第十四届分布式计算及其应用国际学术研讨会

作者： Hongxia Wang Long Zeng Dewei Peng Feng Geng School of Computer Science & Technology Wuhan University of Technology

Through the linear correlation analysis between the local feature and its K-nearest-neighbor visual words and significance testing of locality-constrained linear coding, this paper finds that the fundamental reason for causing nonsignificance of the weight coefficient is the multicollinearity of K-nearest-neighbor visual words in locality-constrained linear coding(LLC) scheme. locality-constrained principal component linear coding can solve the multicollinearity and improves the classification accuracy, but it increases the time overhead of the coding. This paper presents an improved scheme called locality-constrained linear coding based on the principal components of visual vocabulary. To determine the principal components of K-nearest-neighbor visual words of each local feature is simplified to only determine the principal components of visual vocabulary. Experiments have been conducted for comparing and evaluating the proposed method utilizing the Caltech-4 dataset. Experimental results show that locality-constrained linear coding based on the principal components of visual vocabulary reduces the time overhead and the same time it retains the advantages of localityconstrained principal component linear coding.

关键词： visual object categorization local feature linear coding principal components

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