Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency
  Detection   As an important and challenging problem in computer vision, video saliency
detection is typically cast as a spatiotemporal context modeling problem over
consecutive frames. As a result, a key issue in video saliency detection is how
to effectively capture the intrinsical properties of atomic video structures as
well as their associated contextual interactions along the spatial and temporal
dimensions. Motivated by this observation, we propose a graph-theoretic video
saliency detection approach based on adaptive video structure discovery, which
is carried out within a spatiotemporal atomic graph. Through graph-based
manifold propagation, the proposed approach is capable of effectively modeling
the semantically contextual interactions among atomic video structures for
saliency detection while preserving spatial smoothness and temporal
consistency. Experiments demonstrate the effectiveness of the proposed approach
over several benchmark datasets.

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The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: math
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci