Abstract
A Content-based image retrieval (CBIR) framework, which provides a quick retrieval using the SVM-Binary Decision Tree with prefiltering called as Quick SVM-BDT, is proposed. The Support Vector Machine-Pair Wise Coupling (SVM-PWC) and Fuzzy C-Mean (FCM) clustering are the supervised and unsupervised learning techniques respectively, used for the multi-class classification of images. In this system, the SVM based binary decision tree (SVM-BDT) is constructed for semantic learning, and it finds the semantic category of the query image. Similarity matching is done between the query image and only the set of images belonging to the semantic category of the query image. This reduces the search space. The search space is still reduced by prefiltering the images of that particular category which provides a very quick retrieval. Experiments were conducted using the COREL dataset consisting of 1000 images of 10 different semantic categories. The obtained results demonstrate the effectiveness of the proposed framework.
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Felci Rajam, I., Valli, S. (2011). Content-Based Image Retrieval Using a Quick SVM-Binary Decision Tree – QSVMBDT. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Digital Image Processing and Information Technology. DPPR 2011. Communications in Computer and Information Science, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24055-3_2
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DOI: https://doi.org/10.1007/978-3-642-24055-3_2
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