JSEG - Unsupervised Segmentation of Color-Texture Regions in Images

JSEG - Unsupervised Segmentation of Color-Texture Regions in Images

A new method for unsupervised segmentation of color-texture regions in images 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 “Jimage,”
in which high and low values correspond to possible boundaries and interiors of colortexture
regions. A region growing method is then used to segment the image based on the multiscale
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.
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