Paper Info
Title
Learning To Hash With Partial Tags: Exploring Correlation Between Tags And Hashing Bits For Large Scale Image Retrieval
Abstract
Similarity search is an important technique in many large scale vision applications. Hashing approach becomes popular for similarity search due to its computational and memory efficiency. Recently, it has been shown that the hashing quality could be improved by combining supervised information, e.g. semantic tags/labels, into hashing function learning. However, tag information is not fully exploited in existing unsupervised and supervised hashing methods especially when only partial tags are available. This paper proposes a novel semi-supervised tag hashing (SSTH) approach that fully incorporates tag information into learning effective hashing function by exploring the correlation between tags and hashing bits. The hashing function is learned in a unified learning framework by simultaneously ensuring the tag consistency and preserving the similarities between image examples. An iterative coordinate descent algorithm is designed as the optimization procedure. Furthermore, we improve the effectiveness of hashing function through orthogonal transformation by minimizing the quantization error. Extensive experiments on two large scale image datasets demonstrate the superior performance of the proposed approach over several state-of-the-art hashing methods.
Year
DOI
Venue
2014
10.1007/978-3-319-10578-9_25
COMPUTER VISION - ECCV 2014, PT III
Keywords
Field
DocType
Hashing, Tags, Similarity Search, Image Retrieval
Locality-sensitive hashing,Data mining,Hopscotch hashing,Pattern recognition,Computer science,Universal hashing,Feature hashing,K-independent hashing,Artificial intelligence,Dynamic perfect hashing,Hash table,Open addressing
Conference
Volume
ISSN
Citations 
8691
0302-9743
18
PageRank 
References 
Authors
0.67
29
3
Name
Order
Citations
PageRank
Qifan Wang120917.19
Luo Si22498169.52
Dan Zhang346122.17