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http://dx.doi.org/10.3745/JIPS.2013.9.3.349

Interactive Semantic Image Retrieval  

Patil, Pushpa B. (Dept. of Computer Science and Engineering, BLDEA's V. P. Dr. P. G. H. College of Engineering and Technology)
Kokare, Manesh B. (Dept. of Electronics and Telecommunication Engineering, SGGS, Institute of Engineering and Technology)
Publication Information
Journal of Information Processing Systems / v.9, no.3, 2013 , pp. 349-364 More about this Journal
Abstract
The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval performance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.
Keywords
Content-based Image Retrieval (CBIR); Relevance Feedback (RF); Rotated Complex Wavelet Filters (RCWFs); Dual Tree Complex Wavelet; and Image retrieval;
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