NDLI: Lonely but attractive: Sparse color salient points for object retrieval and categorization

Content Provider	IEEE Xplore Digital Library
Author	Stottinger, J. Hanbury, A. Gevers, T. Sebe, N.
Copyright Year	2009
Description	Author affiliation: Vienna Univ. of Technol., Vienna, Austria (Stottinger, J.) \|\| Univ. of Amsterdam, Amsterdam, Netherlands (Hanbury, A.; Gevers, T.; Sebe, N.)
Abstract	Local image descriptors computed in areas around salient points in images are essential for many algorithms in computer vision. Recent work suggests using as many salient points as possible. While sophisticated classifiers have been proposed to cope with the resulting large number of descriptors, processing this large amount of data is computationally costly. In this paper, computational methods are proposed to compute salient points designed to allow a reduction in the number of salient points while maintaining state of the art performance in image retrieval and object recognition applications. To obtain a more sparse description, a color salient point and scale determination framework is proposed operating on color spaces that have useful perceptual and saliency properties. This allows for the necessary discriminative points to be located, allowing a significant reduction in the number of salient points and obtaining an invariant (repeatability) and discriminative (distinctiveness) image description. Experimental results on large image datasets show that the proposed method obtains state of the art results with the number of salient points reduced by half. This reduction in the number of points allows subsequent operations, such as feature extraction and clustering, to run more efficiently. It is shown that the method provides less ambiguous features, a more compact description of visual data, and therefore a faster classification of visual data.
Starting Page	1
Ending Page	8
File Size	2404503
Page Count	8
File Format	PDF
ISBN	9781424439942
ISSN	21607508
DOI	10.1109/CVPRW.2009.5204286
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Image retrieval Color Image sampling Feature extraction Mobile handsets Object recognition Data mining Personal digital assistants Mobile computing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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