Paper Info
Title
Online latent semantic hashing for cross-media retrieval.
Abstract
Hashing based cross-media method has been become an increasingly popular technique in facilitating large-scale multimedia retrieval task, owing to its effectiveness and efficiency. Most existing cross-media hashing methods learn hash functions in a batch based mode. However, in practical applications, data points often emerge in a streaming manner, which makes batch based hashing methods loss their efficiency. In this paper, we propose an Online Latent Semantic Hashing (OLSH) method to address this issue. Only newly arriving multimedia data points are utilized to retrain hash functions efficiently and meanwhile preserve the semantic correlations in old data points. Specifically, for learning discriminative hash codes, discrete labels are mapped to a continuous latent semantic space where the relative semantic distances in data points can be measured more accurately. And then, we propose an online optimization scheme towards the challenging task of learning hash functions efficiently on streaming data points, and the computational complexity and memory cost are much less than the size of training dataset at each round. Extensive experiments across many real-world datasets, e.g. Wiki, Mir-Flickr25K and NUS-WIDE, show the effectiveness and efficiency of the proposed method.
Year
DOI
Venue
2019
10.1016/j.patcog.2018.12.012
Pattern Recognition
Keywords
Field
DocType
Cross-media retrieval,Online learning,Hashing,Latent semantic concept
Data point,Cross media,Online optimization,Artificial intelligence,Hash function,Streaming data,Discriminative model,Semantic hashing,Mathematics,Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
89
1
0031-3203
Citations 
PageRank 
References 
7
0.42
35
Authors
7
Name
Order
Citations
PageRank
Tao Yao1395.33
Gang Wang234497.03
LianShan Yan36414.51
Xiang-Wei Kong421215.09
Qingtang Su517616.90
Caiming Zhang644688.19
Qi Tian76443331.75