cvpr14:irie

Go Irie, Zhenguo Li, Xiao-Ming Wu, Shih-Fu Chang. Locally Linear Hashing for Extracting Non-linear Manifolds. In International Conference on Computer Vison and Pattern Recognition (CVPR), June 2014.

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Abstract

In this paper, we propose a hashing method aiming at reconstructing the locally linear structures of data manifolds in the binary Hamming space, which can be captured by locality-sensitive sparse coding. We cast the problem as a joint minimization of reconstruction error and quantization loss and show that a local optimum can be obtained efficiently via alternating optimization. Our results improve previous state-of-the-art methods by typically 28-74% in semantic retrieval performance, and 627% on the Yale face data

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Zhenguo Li
Xiao-Ming Wu
Shih-Fu Chang

BibTex Reference

@InProceedings{cvpr14:irie,
   Author = {Irie, Go and Li, Zhenguo and Wu, Xiao-Ming and Chang, Shih-Fu},
   Title = {Locally Linear Hashing for Extracting Non-linear Manifolds},
   BookTitle = {International Conference on Computer Vison and Pattern Recognition (CVPR)},
   Month = {June},
   Year = {2014}
}

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