Image segmentation and classification using color features

Color provides a wealth of information for interpretation of image content. The increased availability of affordable digital color cameras has created the opportunity to explore the degree to which color is useful in computer vision. This paper shows that a system for image segmentation and classification can be created using color as the primary feature. This system is comprised of two phases: segmentation and classification. In the first step, an image is searched with a blob detection algorithm to determine the location of any possible foreground elements. These areas are extracted from the image to be used in the next step. Classification is done using a set of eight color features that are optimally selected for each database. The appropriate feature vector is created for each foreground area removed from the original image. The vector is then compared to a preconstructed database to be identified. For this paper USA postage stamps on envelopes were used as the test cases.