  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.
