Salient coding for image classification

Authors:
Yongzhen Huang; Kaiqi Huang; Yinan Yu; Tieniu Tan
Affiliations:
Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing, China;Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing, China;Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing, China;Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing, China
Venue:
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Year:
2011

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Abstract

The codebook based (bag-of-words) model is a widely applied model for image classification. We analyze recent coding strategies in this model, and find that saliency is the fundamental characteristic of coding. The saliency in coding means that if a visual code is much closer to a descriptor than other codes, it will obtain a very strong response. The salient representation under maximum pooling operation leads to the state-of-the-art performance on many databases and competitions. However, most current coding schemes do not recognize the role of salient representation, so that they may lead to large deviations in representing local descriptors. In this paper, we propose "salient coding", which employs the ratio between descriptors' nearest code and other codes to describe descriptors. This approach can guarantee salient representation without deviations. We study salient coding on two sets of image classification databases (15-Scenes and PASCAL VOC2007). The experimental results demonstrate that our approach outperforms all other coding methods in image classification.