DRM: dynamic region matching for image retrieval using probabilistic fuzzy matching and boosting feature selection |
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Authors: | Rongrong Ji Hongxun Yao Dawei Liang |
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Affiliation: | (1) Visual Intelligence Lab, School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin, 150001, China |
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Abstract: | This paper considers the semantic gap in content-based image retrieval from two aspects: (1) irrelevant visual contents (e.g.
background) scatter the mapping from image to human perception; (2) unsupervised feature extraction and similarity ranking
method can not accurately reveal users’ image perception. This paper proposes a novel region-based retrieval framework—dynamic
region matching (DRM) to bridge the semantic gap. (1) To address the first issue, a probabilistic fuzzy region matching algorithm
is adopted to retrieve and match images precisely at object level, which copes with the problem of inaccurate segmentation.
(2) To address the second issue, a “FeatureBoost” algorithm is proposed to construct an effective “eigen” feature set in relevance
feedback (RF) process. And the significance of each region is dynamically updated in RF learning to automatically capture
users’ region of interest (ROI). (3) User’s retrieval purpose is predicted using a novel log-learning algorithm, which predicts
users’ retrieval target in the feature space using the accumulated user operations. Extensive experiments have been conducted
on Corel image database with over 10,000 images. The promising experimental results reveal the effectiveness of our scheme
in bridging the semantic gap. |
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Keywords: | Image retrieval Region matching Relevance feedback AdaBoost Long-term learning |
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