Partial Highest Possible Edge Analysis for Interactive Image Accessibility

Relevance feedback is a technique that takes advantage of human-computer interaction to refine high level queries represented by low level features. Among RF schemes, the most popular technique is SVM based RF scheme. When SVM is used as a classifier in RF, there are two strategies. One strategy is to display the most positive images and use them as the training samples. The most-positive images are chosen as the ones farthest from the boundary on the positive side, plus those nearest from the boundary on the negative side if necessary. Another strategy is that most of SVM based RF scheme does not consider the unlabeled samples even though they are useful in constructing a good classifier. To overcome these drawbacks, in this paper we propose a biased maximum margin analysis (BMMA) and semi supervised BMMA (semiBMMA) for integrating the distinct properties of feedbacks and also to utilize the information of unlabeled samples. The BMMA differentiates positive from negative feedbacks, whereas semiBMMA takes into account the information of unlabeled samples by the introduction of Laplacian regularizer to BMMA. To validate the efficacy of the proposed approach, we test it on both synthesized data and real-world images. Promising results are achieved and this can significantly improve the performance of CBIR systems.