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PKDD
2009
Springer

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

9 years 2 months ago
A Convex Method for Locating Regions of Interest with Multi-instance Learning
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.
Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where PKDD
Authors Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua Zhou
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