Interest Point Detection and Region Descriptors

Abstract

Object recognition in “real-world” scenes – e.g., detect cars in a street image – often is a challenging application due to the large intra-class variety or the presence of heavy background clutter, occlusion, or varying illumination conditions, etc. These tough demands can be met by a two-stage strategy for the description of the image content: the first step consists of the detection of “interest points ” considered to be characteristic. Subsequently, feature vectors also called “region descriptors ” are derived, each representing the image information available in a local neighborhood around one interest point. Object recognition can then be performed by comparing the region descriptors themselves as well as their locations/spatial configuration to the model database. During the last decade, there has been extensive research on this approach to object recognition and many different alternatives for interest point detectors and region descriptors have been suggested. Some of these alternatives are presented in this chapter. It is completed by showing how region descriptors can be used in the field of scene categorization , where the scene shown in an image has to be classified as a whole, e.g., is it of type “city street,” “indoor room,” or “forest”, etc.?