Name
Abstract | ||
|---|---|---|
Image ranking is a critical component in the image search systems, and graph-based ranking has become a promising way to enhance the retrieval effectiveness. Leveraging the clickthrough data to facilitate the ranking is one of the current trends. However, the sparse and noisy properties of the click-through data make the exploitation of such resource difficult. To this end, this paper proposes a click completion solution for graphbased image ranking, which consists of two coupled components. The first one is a click completion algorithm to handle the sparseness. Another one is a soft-label graph ranking solution to exploit the completed click-through data noise-tolerantly. We conduct extensive experiments to evaluate the performance of the proposed scheme for image retrieval, in which encouraging results validate the effectiveness of the proposed techniques. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/PDCAT.2016.044 | 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) |
Keywords | Field | DocType |
graph-based image ranking,image search systems,click completion algorithm,image retrieval | Graph,Ranking,Information retrieval,Ranking SVM,Computer science,Image retrieval,Exploit,Ranking (information retrieval),Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-5082-6 | 0 | 0.34 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohong Qin | 1 | 3 | 2.61 |
| Yu He | 2 | 71 | 11.67 |
| Jun Wu | 3 | 125 | 15.66 |
| Yingpeng Sang | 4 | 21 | 9.05 |