Basic metric learning

Abstract

This report presents a a novel Multiple Kernel Learning (MKL) algorithm for the 1-class support vector machine. The emphasis is placed on viewing the CBIR task with relevance feedback as a metric learning problem, where each image has 11 different feature extraction methods applied to it. Our method attempts at finding the most compact ball amongst the 11 different feature representations using a novel 1- and 2-norm regularisation technique for the 1-class SVM under the MKL framework. We also devise a simple way of including the set of negative examples whilst still utilising the 1-class SVM implementation.

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