Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Partial Membership Latent Dirichlet Allocation   Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting
imagery. However, these models are confined to crisp segmentation, forcing a
visual word (i.e., an image patch) to belong to one and only one topic. Yet,
there are many images in which some regions cannot be assigned a crisp
categorical label (e.g., transition regions between a foggy sky and the ground
or between sand and water at a beach). In these cases, a visual word is best
represented with partial memberships across multiple topics. To address this,
we present a partial membership latent Dirichlet allocation (PM-LDA) model and
an associated parameter estimation algorithm. This model can be useful for
imagery where a visual word may be a mixture of multiple topics. Experimental
results on visual and sonar imagery show that PM-LDA can produce both crisp and
soft semantic image segmentations; a capability previous topic modeling methods
do not have.

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The categories of this paper are: math
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The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: econ