Collaborative Hashing

Hashing technique has become a promising approach for fast similarity search. Most of existing hashing research pursue the binary codes for the same type of entities by preserving their similarities. In practice, there are many scenarios involving nearest neighbor search on the data given in matrix...

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Bibliographic Details
Published in:2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 2147 - 2154
Main Authors: Liu, Xianglong, He, Junfeng, Deng, Cheng, Lang, Bo
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2014
Subjects:
Binary codes
Collaboration
collaborative hashing
Computer vision
Correlation
locality sensitive hashing
matrix hashing
Nearest neighbor searches
Optimization
Pattern recognition
Preserving
Quantization (signal)
Ratings
Search problems
Searching
Similarity
State of the art
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary:Hashing technique has become a promising approach for fast similarity search. Most of existing hashing research pursue the binary codes for the same type of entities by preserving their similarities. In practice, there are many scenarios involving nearest neighbor search on the data given in matrix form, where two different types of, yet naturally associated entities respectively correspond to its two dimensions or views. To fully explore the duality between the two views, we propose a collaborative hashing scheme for the data in matrix form to enable fast search in various applications such as image search using bag of words and recommendation using user-item ratings. By simultaneously preserving both the entity similarities in each view and the interrelationship between views, our collaborative hashing effectively learns the compact binary codes and the explicit hash functions for out-of-sample extension in an alternating optimization way. Extensive evaluations are conducted on three well-known datasets for search inside a single view and search across different views, demonstrating that our proposed method outperforms state-of-the-art baselines, with significant accuracy gains ranging from 7.67% to 45.87% relatively.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2014.275