Compact Representation for Image Classification: To Choose or to Compress?

Yu Zhang, Jianxin Wu, Jianfei Cai; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 907-914

Abstract


In large scale image classification, features such as Fisher vector or VLAD have achieved state-of-the-art results. However, the combination of large number of examples and high dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper argues that feature selection is a better choice than feature compression. We show that strong multicollinearity among feature dimensions may not exist, which undermines feature compression's effectiveness and renders feature selection a natural choice. We also show that many dimensions are noise and throwing them away is helpful for classification. We propose a supervised mutual information (MI) based importance sorting algorithm to choose features. Combining with 1-bit quantization, MI feature selection has achieved both higher accuracy and less computational cost than feature compression methods such as product quantization and BPBC.

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[bibtex]
@InProceedings{Zhang_2014_CVPR,
author = {Zhang, Yu and Wu, Jianxin and Cai, Jianfei},
title = {Compact Representation for Image Classification: To Choose or to Compress?},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}