Image Categorization by Learning and Reasoning with Regions

Yixin Chen
University of New Orleans

James Z. Wang
The Pennsylvania State University

Designing computer programs to automatically categorize images using low-level features is a challenging research topic in computer vision. In this paper, we present a new learning technique, which extends Multiple-Instance Learning (MIL), and its application to the problem of region-based image categorization. Images are viewed as bags, each of which contains a number of instances corresponding to regions obtained from image segmentation. The standard MIL problem assumes that a bag is labeled positive if at least one of its instances is positive; otherwise, the bag is negative. In the proposed MIL framework, DDSVM, a bag label is determined by some number of instances satisfying various properties. DD-SVM rst learns a collection of instance prototypes according to a Diverse Density (DD) function. Each instance prototype represents a class of instances that is more likely to appear in bags with the specific label than in the other bags. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new feature space, named the bag feature space. Finally, standard support vector machines are trained in the bag feature space. We provide experimental results on an image categorization problem and a drug activity prediction problem.

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Citation: Yixin Chen and James Z. Wang, ``Image Categorization by Learning and Reasoning with Regions,'' Journal of Machine Learning Research, vol. 5, 913-939, August 2004.

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Last Modified: August 6, 2004