Record number :
1697159
Title of article :
Semi-supervised multi-graph hashing for scalable similarity search
Author/Authors :
Cheng، نويسنده , , Qi-Jian and Leng، نويسنده , , Cong and Li، نويسنده , , Peng and Wang، نويسنده , , Meng and Lu، نويسنده , , Hanqing، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Pages :
10
From page :
12
To page :
21
Abstract :
Due to the explosive growth of the multimedia contents in recent years, scalable similarity search has attracted considerable attention in many large-scale multimedia applications. Among the different similarity search approaches, hashing based approximate nearest neighbor (ANN) search has become very popular owing to its computational and storage efficiency. However, most of the existing hashing methods usually adopt a single modality or integrate multiple modalities simply without exploiting the effect of different features. To address the problem of learning compact hashing codes with multiple modality, we propose a semi-supervised Multi-Graph Hashing (MGH) framework in this paper. Different from the traditional methods, our approach can effectively integrate the multiple modalities with optimized weights in a multi-graph learning scheme. In this way, the effects of different modalities can be adaptively modulated. Besides, semi-supervised information is also incorporated into the unified framework and a sequential learning scheme is adopted to learn complementary hash functions. The proposed framework enables direct and fast handling for the query examples. Thus, the binary codes learned by our approach can be more effective for fast similarity search. Extensive experiments are conducted on two large public datasets to evaluate the performance of our approach and the results demonstrate that the proposed approach achieves promising results compared to the state-of-the-art methods.
Keywords :
semi-supervised learning , hashing , Multiple graph learning , Multiple modality
Journal title :
Computer Vision and Image Understanding
Journal title :
Computer Vision and Image Understanding
Serial Year :
2014
Link To Document :
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