NDLI: Scene classification with a sparse set of salient regions

Content Provider	IEEE Xplore Digital Library
Author	Borji, A. Itti, L.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, University of Southern California, Hedco Neuroscience Building, 3641 Watt Way, Los Angeles, 90089-2520, USA (Borji, A.; Itti, L.)
Abstract	This work proposes an approach for scene classification by extracting and matching visual features only at the focuses of visual attention instead of the entire scene. Analysis over a database of natural scenes demonstrates that regions proposed by the saliency-based model of visual attention are robust to image transformations. Using a nearest neighbor classifier and a distance measure defined over the salient regions, we obtained 97.35% and 78.28% classification rates with SIFT and C2 features from the HMAX model at 5 salient regions covering at most 31% of the image. Classification with features extracted from the entire image results in 99.3% and 82.32% using SIFT and C2 features, respectively. Comparing attentional and adhoc approaches shows that classification rate of the first approach is 0.95 of the second. Overall, our results prove that efficient scene classification, in terms of reducing the complexity of feature extraction is possible without a significant drop in performance.
Starting Page	1902
Ending Page	1908
File Size	1993911
Page Count	7
File Format	PDF
ISBN	9781612843865
ISSN	10504729
e-ISBN	9781612843858
DOI	10.1109/ICRA.2011.5979815
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Visualization Computational modeling Detectors Image recognition Computational complexity Biology
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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