Image Categorization by Learning and Reasoning with Regions.pdf

Journal of Machine Learning Research 5 (2004) 913–939 Submitted 7/03; Revised 11/03; Pulished 8/04 Image Categorization by Learning and Reasoning with Regions Yixin Chen YIXIN @C S .UN O .EDU Department of Computer Science University of New Orleans New Orleans, LA 70148, USA James Z. Wang JWAN G @I ST.P SU .EDU School of Information Sciences and Technology The Pennsylvania State University University Park, PA 16802, USA Editor: Donald Geman Abstract Designing computer programs to automatically categorize images using low-level features is a chal- lenging 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 im- age 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, DD-SVM, a bag label is determined by some number of instances satisfying various properties. DD-SVM first learns a collection of instance pr ototypes 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 featur e space. Finally, stan