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COLOR IMAGE RETRIEVAL USING MULTISPECTRAL RANDOM FIELD TEXTURE MODELS

This paper describes a system to query large color image databases to find similar images to a target image presented during the query action. The approach is to use color and texture information to index and match the images in the database. The texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The color is represented by ratios of sample color means. The retrieval process involves segmenting the image into regions of uniform texture/color using an unsupervised histogram clustering approach that utilizes MSAR and color features. The current system is capable of retrieving images that contain same texture(s)/color to a query image. Work is in progress to develop more sophisticated retrieval algorithms based on metrics defined on the segmented regions.