Abstract
This paper presents content-based image retrieval frameworks with relevance feedback based on AdaBoost learning method. As we know relevance feedback (RF) is online process, so we have optimized the learning process by considering the most positive image selection on each feedback iteration. To learn the system we have used AdaBoost. The main significances of our system are to address the small training sample and to reduce retrieval time. Experiments are conducted on 1856 texture images to demonstrate the effectiveness of the proposed framework. These experiments employed large image databases and combined RCWFs and DT-CWT texture descriptors to represent content of the images.
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Patil, P.B., Kokare, M. (2011). Semantic Learning in Interactive Image Retrieval. 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_12
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DOI: https://doi.org/10.1007/978-3-642-24055-3_12
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