Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Learning-Based Visual Saliency Fusion Model for High Dynamic Range
  Video (LBVS-HDR)   Saliency prediction for Standard Dynamic Range (SDR) videos has been well
explored in the last decade. However, limited studies are available on High
Dynamic Range (HDR) Visual Attention Models (VAMs). Considering that the
characteristic of HDR content in terms of dynamic range and color gamut is
quite different than those of SDR content, it is essential to identify the
importance of different saliency attributes of HDR videos for designing a VAM
and understand how to combine these features. To this end we propose a
learning-based visual saliency fusion method for HDR content (LVBS-HDR) to
combine various visual saliency features. In our approach various conspicuity
maps are extracted from HDR data, and then for fusing conspicuity maps, a
Random Forests algorithm is used to train a model based on the collected data
from an eye-tracking experiment. Performance evaluations demonstrate the
superiority of the proposed fusion method against other existing fusion
methods.

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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn