[Comp-neuro] A Neural System for Natural Scene Classification
steve at cns.bu.edu
Sat Mar 8 03:07:44 CET 2008
The following article is now available at
Grossberg, S. and Huang, T.-R.
ARTSCENE: A Neural System for Natural Scene Classification.
Journal of Vision, in press.
How do humans rapidly recognize a scene? How can neural models
capture this biological competence to achieve state-of-the-art scene
classification? The ARTSCENE neural system classifies natural scene
photographs by using multiple spatial scales to efficiently
accumulate evidence for gist and texture. ARTSCENE embodies a
coarse-to-fine Texture Size Ranking Principle whereby spatial
attention processes multiple scales of scenic information, from
global gist to local textures, to learn and recognize scenic
properties. The model can incrementally learn and rapidly predict
scene identity by gist information alone, and then accumulate learned
evidence from scenic textures to refine this hypothesis. The model
shows how texture-fitting allocations of spatial attention, called
attentional shrouds, can facilitate scene recognition, particularly
when they include a border of adjacent textures. Using grid gist plus
three shroud textures on a benchmark photograph dataset, ARTSCENE
discriminates 4 landscape scene categories (coast, forest, mountain,
and countryside) with up to 91.85% correct on a test set, outperforms
alternative models in the literature which use biologically
implausible computations, and outperforms component systems that use
either gist or texture information alone.
KEYWORDS: scene classification; gist; texture; spatial attention;
coarse-to-fine processing; attentional shroud; multiple-scale
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