An image retrieval system using multispectral random field models, color, and geometric features

This paper describes a novel color texture-based image retrieval system for the query of an image database to find similar images to a target image. The retrieval process involves segmenting the image into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the combination of multispectral simultaneous auto regressive (MSAR) and color features. The color texture content, location, area and shape of the segmented regions are used to develop similarity measures describing the closeness of a query image to database images. These attributes are derived from the maximum fitting square and best fitting ellipse to each of the segmented regions. The proposed similarity measure combines all these attributes to rank the closeness of the images. The performance of the system is tested on two databases containing synthetic mosaics of natural textures and natural scenes, respectively.