Name
Abstract | ||
|---|---|---|
One of the challenges in image search is to learn with few labeled examples. Existing solutions mainly focus on leveraging either unlabeled data or query logs to address this issue, but little is known in taking both into account. This work presents a novel learning scheme that exploits both unlabeled data and query logs through a unified Manifold Ranking (MR) framework. In particular, we propose a local scaling technique to facilitate MR by self-tuning the scale parameter, and a soft label propagation strategy to enhance the robustness of MR against erroneous query logs. Further, within the proposed MR framework, a hybrid active learning method is developed, which is effective and efficient to select the informative and representative unlabeled examples, so as to maximally reduce users' labeling effort. An empirical study shows that the proposed scheme is significantly more effective than the state-of-the-art approaches. (C) 2013 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.compeleceng.2013.09.004 | Computers & Electrical Engineering |
Field | DocType | Volume |
Data mining,Active learning,Computer science,Label propagation,Robustness (computer science),Exploit,Artificial intelligence,Manifold ranking,Machine learning,Empirical research,Scale parameter | Journal | 40 |
Issue | ISSN | Citations |
3 | 0045-7906 | 2 |
PageRank | References | Authors |
0.37 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Wu | 1 | 125 | 15.66 |
| Zhi-Bo Xiao | 2 | 25 | 1.01 |
| Hai-Shuai Wang | 3 | 62 | 13.11 |
| Hong Shen | 4 | 499 | 52.98 |