10 January 2018 Learning binary code via PCA of angle projection for image retrieval
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106053Y (2018) https://doi.org/10.1117/12.2296255
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
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.
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Fumeng Yang, Zhiqiang Ye, Xueqi Wei, Congzhong Wu, "Learning binary code via PCA of angle projection for image retrieval ", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053Y (10 January 2018); doi: 10.1117/12.2296255; https://doi.org/10.1117/12.2296255
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