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Improving scale invariant feature transform with local color contrastive descriptor for image classification

[+] Author Affiliations
Sheng Guo

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

University of Chinese Academy of Sciences, Shenzhen College of Advanced Technology, Beijing, China

Weilin Huang

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

University of Oxford, Department of Engineering Science, United Kingdom

Yu Qiao

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

The Chinese University of Hong Kong, Hong Kong

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, The Guangdong Key Laboratory of Computer Vision and Virtual Reality, Shenzhen, China

J. Electron. Imaging. 26(1), 013015 (Feb 15, 2017). doi:10.1117/1.JEI.26.1.013015
History: Received August 22, 2016; Accepted January 26, 2017
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Abstract.  Image representation and classification are two fundamental tasks toward version understanding. Shape and texture provide two key features for visual representation and have been widely exploited in a number of successful local descriptors, e.g., scale invariant feature transform (SIFT), local binary pattern descriptor, and histogram of oriented gradient. Unlike these gradient-based descriptors, this paper presents a simple yet efficient local descriptor, named local color contrastive descriptor (LCCD), which captures the contrastive aspects among local regions or color channels for image representation. LCCD is partly inspired by the neural science facts that color contrast plays important roles in visual perception and there exist strong linkages between color and shape. We leverage f-divergence as a robust measure to estimate the contrastive features between different spatial locations and multiple channels. Our descriptor enriches local image representation with both color and contrast information. Due to that LCCD does not explore any gradient information, individual LCCD does not yield strong performance. But we verified experimentally that LCCD can compensate strongly SIFT. Extensive experimental results on image classification show that our descriptor improves the performance of SIFT substantially by combination on three challenging benchmarks, including MIT Indoor-67 database, SUN397, and PASCAL VOC 2007.

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Citation

Sheng Guo ; Weilin Huang and Yu Qiao
"Improving scale invariant feature transform with local color contrastive descriptor for image classification", J. Electron. Imaging. 26(1), 013015 (Feb 15, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.1.013015


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