A fast texture feature extraction method for region-based image segmentation

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In region-based image segmentation, an image is partitioned into connected regions by grouping neighboring pixels of similar features. To achieve fine-grain segmentation at the pixel level, we must be able to define features on a per-pixel basis. Typically for individual pixels, texture feature extraction is very computationally intensive. In this paper, we propose a new hierarchical method to reduce the computational complexity and expedite texture feature extraction, by taking advantage of the similarities between the neighboring pixels. In our method, an image is divided into blocks of pixels of different granularities at the various levels of the hierarchy. A representative pixel is used to describe the texture within a block. Each pixel within a block gets its texture feature values either by copying the corresponding representative pixel’s texture features, if its features are deemed sufficiently similar, or by computing its own texture features if it is a representative pixel itself. This way, we extract texture features for each pixel in the image with the minimal amount of texture feature extraction computation. The experiments demonstrate the good performance of our method, which can reduce 30% to 60% of the computational time while keeping the distortions in the range of 0.6% to 3.7%. By tailoring the texture feature extraction threshold, we can balance the tradeoff between extraction speed and distortion according to the each system’s specific needs.