Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamical And-Or Graph Learning for Object Shape Modeling and Detection   This paper studies a novel discriminative part-based model to represent and
recognize object shapes with an "And-Or graph". We define this model consisting
of three layers: the leaf-nodes with collaborative edges for localizing local
parts, the or-nodes specifying the switch of leaf-nodes, and the root-node
encoding the global verification. A discriminative learning algorithm, extended
from the CCCP [23], is proposed to train the model in a dynamical manner: the
model structure (e.g., the configuration of the leaf-nodes associated with the
or-nodes) is automatically determined with optimizing the multi-layer
parameters during the iteration. The advantages of our method are two-fold. (i)
The And-Or graph model enables us to handle well large intra-class variance and
background clutters for object shape detection from images. (ii) The proposed
learning algorithm is able to obtain the And-Or graph representation without
requiring elaborate supervision and initialization. We validate the proposed
method on several challenging databases (e.g., INRIA-Horse, ETHZ-Shape, and
UIUC-People), and it outperforms the state-of-the-arts approaches.

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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph