Learning Binary Codes for High-Dimensional Data Using Bilinear Projections

Yunchao Gong, Sanjiv Kumar, Henry A. Rowley, Svetlana Lazebnik; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 484-491

Abstract


Recent advances in visual recognition indicate that to achieve good retrieval and classification accuracy on largescale datasets like ImageNet, extremely high-dimensional visual descriptors, e.g., Fisher Vectors, are needed. We present a novel method for converting such descriptors to compact similarity-preserving binary codes that exploits their natural matrix structure to reduce their dimensionality using compact bilinear projections instead of a single large projection matrix. This method achieves comparable retrieval and classification accuracy to the original descriptors and to the state-of-the-art Product Quantization approach while having orders of magnitude faster code generation time and smaller memory footprint.

Related Material


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[bibtex]
@InProceedings{Gong_2013_CVPR,
author = {Gong, Yunchao and Kumar, Sanjiv and Rowley, Henry A. and Lazebnik, Svetlana},
title = {Learning Binary Codes for High-Dimensional Data Using Bilinear Projections},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}