We propose a new approach for image segmentation that is based on low-level features for color and texture. It is aimed at segmentation of natural scenes, in which color and texture do not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust, and at the same time, accurate and precise segmentations. Key segmentation parameters are determined on the basis of subjective tests. The resulting segmentations convey semantic information that can be used for content-based retrieval. The performance of the proposed algorithms is demonstrated in the domain of photographic images, including low resolution, degraded, and compressed images.

Thrasyvoulos (Thrasos) Pappas received the S.B., S.M., and Ph.D. degrees in electrical engineering and computer science from MIT in 1979, 1982, and 1987, respectively. From 1987 until 1999, he was a Member of the Technical Staff at Bell Laboratories, Murray Hill, NJ. In September 1999, he joined the Department of Electrical and Computer Engineering at Northwestern University as an associate professor. His research interests are in image and video compression, perceptual models for image processing, model-based halftoning, image and video analysis, and multimedia signal processing. Dr. Pappas has served as chair of the IEEE Image and Multidimensional Signal Processing Technical Committee, associate editor of the IEEE Transactions on Image Processing, technical program co-chair of ICIP-01 and the Symposium on Information Processing in Sensor Networks (IPSN-04), and since 1997 he has been co-chair of the SPIE/IS&T Conference on Human Vision and Electronic Imaging.