A Convex Method for Locating Regions of Interest with Multi-instance Learning

Authors:
Yu-Feng Li;James T. Kwok;Ivor W. Tsang;Zhi-Hua Zhou
Affiliations:
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 210093;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China;School of Computer Engineering, Nanyang Technological University, Singapore 639798;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 210093
Venue:
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Year:
2009

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Abstract

In content-based image retrieval (CBIR) and image screening, it is often desirable to locate the regions of interest (ROI) in the images automatically. This can be accomplished with multi-instance learning techniques by treating each image as a bag of instances (regions). Many SVM-based methods are successful in predicting the bag labels, however, few of them can locate the ROIs. Moreover, they are often based on either local search or an EM-style strategy, and may get stuck in local minima easily. In this paper, we propose two convex optimization methods which maximize the margin of concepts via key instance generation at the instance-level and bag-level, respectively. Our formulation can be solved efficiently with a cutting plane algorithm. Experiments show that the proposed methods can effectively locate ROIs, and they also achieve performances competitive with state-of-the-art algorithms on benchmark data sets.