Unsupervised segmentation of color-texture regions in images and video

TLDR

The paper proposes an unsupervised method for segmenting color‑texture regions in images and video, focusing on spatial segmentation with a criterion for good segmentation based on a class‑map. JSEG first quantizes image colors into representative classes to form a class‑map, then applies a local‑window criterion to generate a J‑image, and finally uses multiscale region growing—extended to video with embedded tracking—to segment color‑texture regions. Experiments demonstrate that JSEG robustly segments color‑texture regions in real images and video.

Abstract

A 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 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 nonrigid object motion. Experiments show the robustness of the JSEG algorithm on real images and video.