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Feature Coding in Image Classification: A Comprehensive Study
March 2014 (vol. 36 no. 3)
pp. 493-506
Yongzhen Huang, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing
Zifeng Wu, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing
Liang Wang, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing
Tieniu Tan, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing
Image classification is a hot topic in computer vision and pattern recognition. Feature coding, as a key component of image classification, has been widely studied over the past several years, and a number of coding algorithms have been proposed. However, there is no comprehensive study concerning the connections between different coding methods, especially how they have evolved. In this paper, we first make a survey on various feature coding methods, including their motivations and mathematical representations, and then exploit their relations, based on which a taxonomy is proposed to reveal their evolution. Further, we summarize the main characteristics of current algorithms, each of which is shared by several coding strategies. Finally, we choose several representatives from different kinds of coding approaches and empirically evaluate them with respect to the size of the codebook and the number of training samples on several widely used databases (15-Scenes, Caltech-256, PASCAL VOC07, and SUN397). Experimental findings firmly justify our theoretical analysis, which is expected to benefit both practical applications and future research.
Index Terms:
bag-of-features,Image classification,feature coding
Citation:
Yongzhen Huang, Zifeng Wu, Liang Wang, Tieniu Tan, "Feature Coding in Image Classification: A Comprehensive Study," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3, pp. 493-506, March 2014, doi:10.1109/TPAMI.2013.113
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