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Proceedings Paper

Learning binary code via PCA of angle projection for image retrieval
Author(s): Fumeng Yang; Zhiqiang Ye M.D.; Xueqi Wei M.D.; Congzhong Wu
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Paper Abstract

With benefits of low storage costs and high query speeds, binary code representation methods are widely researched for efficiently retrieving large-scale data. In image hashing method, learning hashing function to embed highdimensions feature to Hamming space is a key step for accuracy retrieval. Principal component analysis (PCA) technical is widely used in compact hashing methods, and most these hashing methods adopt PCA projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit by thresholding. The variances of different projected dimensions are different, and with real-valued projection produced more quantization error. To avoid the real-valued projection with large quantization error, in this paper we proposed to use Cosine similarity projection for each dimensions, the angle projection can keep the original structure and more compact with the Cosine-valued. We used our method combined the ITQ hashing algorithm, and the extensive experiments on the public CIFAR-10 and Caltech-256 datasets validate the effectiveness of the proposed method.

Paper Details

Date Published: 10 January 2018
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053Y (10 January 2018); doi: 10.1117/12.2296255
Show Author Affiliations
Fumeng Yang, Anhui Institute of Information Technology (China)
Zhiqiang Ye M.D., Hefei Univ. of Technology (China)
Xueqi Wei M.D., Hefei Univ. of Technology (China)
Congzhong Wu, Heifei Univ. of Technology (China)

Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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