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Biased Maximum Margin Analysis for Content based Interactive Image Retrieval


Satish kumar Suresh.M.B


Vol. 15  No. 8  pp. 67-71


Content based image retrieval from large resources has become an area of wide interest now a days in many applications. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high level semantic concepts, and thus to improve the performance of CBIR systems. Content-based image retrieval system that uses colour, edge and texture as visual features to describe the content of an image region. Using SVM as an RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, most of the SVM-based RF techniques do not take into account the unlabeled samples, although they are very helpful in constructing a good classifier. To explore solutions to overcome these two drawbacks, Biased maximum margin analysis and a semi supervised biased maximum margin analysis for integrating the distinct properties of feedbacks and utilizing the information of unlabeled samples for SVM-based RF schemes. The Biased maximum margin analysis differentiates positive feedbacks from negative ones based on local analysis whereas the semi supervised biased maximum margin analysis can effectively integrate information of unlabeled samples by introducing a Laplacian regularizer to the biased maximum margin analysis


Image retrieval, margin