Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Texture Categorization with Biologically Inspired Filtering   Within the domain of texture classification, a lot of effort has been spent
on local descriptors, leading to many powerful algorithms. However,
preprocessing techniques have received much less attention despite their
important potential for improving the overall classification performance. We
address this question by proposing a novel, simple, yet very powerful
biologically-inspired filtering (BF) which simulates the performance of human
retina. In the proposed approach, given a texture image, after applying a DoG
filter to detect the "edges", we first split the filtered image into two "maps"
alongside the sides of its edges. The feature extraction step is then carried
out on the two "maps" instead of the input image. Our algorithm has several
advantages such as simplicity, robustness to illumination and noise, and
discriminative power. Experimental results on three large texture databases
show that with an extremely low computational cost, the proposed method
improves significantly the performance of many texture classification systems,
notably in noisy environments. The source codes of the proposed algorithm can
be downloaded from https://sites.google.com/site/nsonvu/code.

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The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
16
The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: eess