Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Superpixel Segmentation Using Gaussian Mixture Model   Superpixel segmentation algorithms are to partition an image into
perceptually coherence atomic regions by assigning every pixel a superpixel
label. Those algorithms have been wildly used as a preprocessing step in
computer vision works, as they can enormously reduce the number of entries of
subsequent algorithms. In this work, we propose an alternative superpixel
segmentation method based on Gaussian mixture model (GMM) by assuming that each
superpixel corresponds to a Gaussian distribution, and assuming that each pixel
is generated by first randomly choosing one distribution from several Gaussian
distributions which are defined to be related to that pixel, and then the pixel
is drawn from the selected distribution. Based on this assumption, each pixel
is supposed to be drawn from a mixture of Gaussian distributions with unknown
parameters (GMM). An algorithm based on expectation-maximization method is
applied to estimate the unknown parameters. Once the unknown parameters are
obtained, the superpixel label of a pixel is determined by a posterior
probability. The success of applying GMM to superpixel segmentation depends on
the two major differences between the traditional GMM-based clustering and the
proposed one: data points in our model may be non-identically distributed, and
we present an approach to control the shape of the estimated Gaussian functions
by adjusting their covariance matrices. Our method is of linear complexity with
respect to the number of pixels. The proposed algorithm is inherently parallel
and can get faster speed by adding simple OpenMP directives to our
implementation. According to our experiments, our algorithm outperforms the
state-of-the-art superpixel algorithms in accuracy and presents a competitive
performance in computational efficiency.

15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat