Name
Abstract | ||
|---|---|---|
The development of social media brings great challenges to image retrieval on both efficiency and accuracy. In addition to achieving fast similarity search over large scale data, it is very crucial to represent the complex and high-order relationships among the social contents to improve the semantic understanding of social images.In this paper, unified hypergraph is implemented to model the various relationships among images and other contexts in social media. Moreover, we extend traditional spectral hashing to hypergraph to accelerate similarity search of social images by mapping semantically related vertices into similar binary codes within a short Hamming distance. Furthermore, the proposed HSH approach is extended to out-of-sample data in a supervised manner. We evaluated our approach on the dataset crawled from Flickr and the experiment results indicate that our proposed HSH approach is both efficient and effective. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2072298.2072039 | ACM Multimedia 2001 |
Keywords | Field | DocType |
large scale data,unified hypergraph,fast similarity search,great challenge,social media,experiment result,social image,hypergraph spectral,social content,similarity search,proposed hsh approach,image retrieval,hashing,hamming distance | Social media,Information retrieval,Vertex (geometry),Computer science,Binary code,Hypergraph,Image retrieval,Hamming distance,Hash function,Nearest neighbor search | Conference |
Citations | PageRank | References |
19 | 0.67 | 9 |
Authors | ||
5 | ||