Region-based image categorization with reduced feature set

Abstract

In this paper we propose a new algorithm for region-based image categorization that is formulated as a multiple instance learning (MIL) problem. The proposed algorithm transforms the MIL problem into a traditional supervised learning problem, and solves it using a standard supervised learning method. The features used in the proposed algorithm are the hyperclique patterns which are ldquocondensedrdquo into a small set of discriminative features. Each hyperclique pattern consists of multiple strongly-correlated instances (i.e., features). As a result, hyperclique patterns are able to capture the information that are not shared by individual features. The advantages of the proposed algorithm over existing algorithms are threefold: (i) unlike some existing algorithms which use learning methods that are specifically designed for MIL or for certain datasets, the proposed algorithm uses a general-purpose standard supervised learning method, (ii) it uses a significantly small set of features which are empirically more discriminative than the PCA features (i.e. principal components), and (iii) it is simple and efficient and achieves a comparable performance to most state-of-the-art algorithms. The efficiency and good performance of the proposed algorithm make it a practical solution to general MIL problems. In this paper, we apply the proposed algorithm to both drug activity prediction and image categorization, and promising results are obtained.