Abstract
Previous efforts in hashing intend to preserve data variance or pairwise affinity, but neither is adequate in capturing the manifold structures hidden in most visual data. In this paper, we tackle this problem by reconstructing the locally linear structures of manifolds in the binary Hamming space, which can be learned by locality-sensitive sparse coding. We cast the problem as a joint minimization of reconstruction error and quantization loss, and show that, despite its NP-hardness, a local optimum can be obtained efficiently via alternative optimization. Our method distinguishes itself from existing methods in its remarkable ability to extract the nearest neighbors of the query from the same manifold, instead of from the ambient space. On extensive experiments on various image benchmarks, our results improve previous state-of-the-art by 28-74% typically, and 627% on the Yale face data.
Original language | English |
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Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Publisher | IEEE Computer Society |
Pages | 2123-2130 |
Number of pages | 8 |
ISBN (Electronic) | 9781479951178 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Externally published | Yes |
Event | 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States Duration: 23 Jun 2014 → 28 Jun 2014 |
Conference
Conference | 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 |
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Country | United States |
City | Columbus |
Period | 23/06/14 → 28/06/14 |
Keywords
- hashing
- local linearity
- manifold
- retrieval
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition