Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A regularization-based approach for unsupervised image segmentation   We propose a novel unsupervised image segmentation algorithm, which aims to
segment an image into several coherent parts. It requires no user input, no
supervised learning phase and assumes an unknown number of segments. It
achieves this by first over-segmenting the image into several hundred
superpixels. These are iteratively joined on the basis of a discriminative
classifier trained on color and texture information obtained from each
superpixel. The output of the classifier is regularized by a Markov random
field that lends more influence to neighbouring superpixels that are more
similar. In each iteration, similar superpixels fall under the same label,
until only a few coherent regions remain in the image. The algorithm was tested
on a standard evaluation data set, where it performs on par with
state-of-the-art algorithms in term of precision and greatly outperforms the
state of the art by reducing the oversegmentation of the object of interest.

12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math