Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching   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.

33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math