Unsupervised Segmentation of Color-Texture Regions
in Images and Video

Yining Deng and B. S. Manjunath

Abstract

A new method for unsupervised segmentation of color-texture regions
in images and video is presented. This method, which we refer to as JSEG,
consists of two independent steps: color quantization
and spatial segmentation. In the first step, colors in the image
are quantized to several representative classes that can be used
to differentiate regions in the image.
The image pixels are then replaced by their corresponding
color class labels, thus forming a class-map of the image.
The focus of this work is on spatial segmentation,
where a criterion for "good" segmentation using the class-map is proposed.
Applying the criterion to local windows in the class-map results
in the "J-image," in which high and low values correspond to
possible boundaries and interiors of color-texture regions.
A region growing method is then used to segment the image
based on the multi-scale J-images. A similar approach is applied to
video sequences. An additional region tracking scheme is embedded
into the region growing process to achieve consistent segmentation
and tracking results, even for scenes with non-rigid object motion.
Experiments show the robustness of the JSEG algorithm on real images and video.