Abstract
Efficient image retrieval from a large image repository is still a challenging task because of the semantic gap. In this paper, a stride is made towards reducing the semantic gap by proposing an efficient approach using relevance feedback and random forest-based AdaBoost learning. Initially, user feedback is used to move the query point more towards the relevant images. The user feedback is also used to train the random forest classifier, for learning the user retrieval intention. Then AdaBoost learning is used to identify the weak classifiers and to assign more weights to weak classifiers in the weighted majority voting scheme. AdaBoost learning is adopted to overcome the prediction variance of the random forest classifier. The experimental evaluation is performed on two different real world image databases and shows that the proposed approach is more efficient, as an average precision of 95% is achieved in six iterations of relevance feedback.
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