Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching
This work addresses a specific challenge in computer vision for applications requiring texture-aware superpixel decomposition, but it is incremental as it builds on existing superpixel methods.
The paper tackles the problem of clustering image pixels by local texture in superpixel generation, proposing a nearest neighbor-based method that achieves favorable segmentation performance on standard datasets and improves computational efficiency compared to recent texture-aware approaches.
Superpixels are widely used in computer vision applications. Nevertheless, decomposition methods may still fail to efficiently cluster image pixels according to their local texture. In this paper, we propose a new Nearest Neighbor-based Superpixel Clustering (NNSC) method to generate texture-aware superpixels in a limited computational time compared to previous approaches. We introduce a new clustering framework using patch-based nearest neighbor matching, while most existing methods are based on a pixel-wise K-means clustering. Therefore, we directly group pixels in the patch space enabling to capture texture information. We demonstrate the efficiency of our method with favorable comparison in terms of segmentation performances on both standard color and texture datasets. We also show the computational efficiency of NNSC compared to recent texture-aware superpixel methods.