Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Constrained Dominant sets and Its applications in computer vision   In this thesis, we present new schemes which leverage a constrained
clustering method to solve several computer vision tasks ranging from image
retrieval, image segmentation and co-segmentation, to person re-identification.
In the last decades clustering methods have played a vital role in computer
vision applications; herein, we focus on the extension, reformulation, and
integration of a well-known graph and game theoretic clustering method known as
Dominant Sets. Thus, we have demonstrated the validity of the proposed methods
with extensive experiments which are conducted on several benchmark datasets.

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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol