Name
Abstract | ||
|---|---|---|
The proper use of constraints improves the data clustering performance. In this paper, we propose a novel interactive image retrieval framework using constraints. First, we extract the user s region of interest (ROI) from queries by simple user interaction using adaptive constraints-based seed propagation (ACSP), and obtain initial retrieval results based on the ROI. Then, we improve the retrieval results by active learning from the user s relevance feedback using ACSP. Since ACSP is very effective in propagating the user s interactive information of constraints by employing a kernel learning strategy, it successfully learns the correlation between low-level image features and high-level semantics from the ROI and relevance feedbacks. Experimental results demonstrate that the proposed framework remarkably improves the image retrieval performance by ACSP-based constraint propagation in terms of both effectiveness and efficiency. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2015.02.040 | Neurocomputing |
Keywords | Field | DocType |
Active learning,Adaptive constraint propagation,Interactive image retrieval,Pairwise constraints,Relevance feedback,Seed propagation | Kernel (linear algebra),Local consistency,Active learning,Relevance feedback,Computer science,Feature (computer vision),Image retrieval,Artificial intelligence,Region of interest,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
161 | C | 0925-2312 |
Citations | PageRank | References |
5 | 0.40 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Jian | 1 | 59 | 8.07 |
| Cheolkon Jung | 2 | 342 | 47.75 |
| Yanbo Shen | 3 | 26 | 2.73 |
| Juan Liu | 4 | 1128 | 145.32 |