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Interactive image retrieval using constraints
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DOI：10.1016/j.neucom.2015.02.040.png)
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Abstract
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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. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Active learning
Adaptive constraint propagation
Interactive image retrieval
Pairwise constraints
Relevance feedback
Seed propagation
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