Perceptual Feature Selection for Semantic Image Classification

Content-based image retrieval has become an indispensable tool for managing the rapidly growing collections of digital images. The goal is to organize the contents semantically, according to meaningful categories. In recent papers we introduced a new approach for semantic image classification that relies on the adaptive perceptual color-texture segmentation algorithm proposed by Chen et al. This algorithm combines knowledge of human perception and signal characteristics to segment natural scenes into perceptually uniform regions. The resulting segments can be classified into semantic categories using region-wide features as medium level descriptors. Such descriptors are the key to bridging the gap between low-level image primitives and high-level image semantics. The segment classification is based on linear discriminant analysis techniques. In this paper, we examine the classification performance (precision and recall rates) when different sets of region-wide features are used. These include different color composition features, spatial texture, and segment location. We demonstrate the effectiveness of the proposed techniques on a database that includes 9000 segments from approximately 2500 photographs of natural scenes.