NDLI: Unsupervised modeling of object categories using link analysis techniques

Content Provider	IEEE Xplore Digital Library
Author	Gunhee Kim Faloutsos, C. Hebert, M.
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA (Gunhee Kim; Faloutsos, C.; Hebert, M.)
Abstract	We propose an approach for learning visual models of object categories in an unsupervised manner in which we first build a large-scale complex network which captures the interactions of all unit visual features across the entire training set and we infer information, such as which features are in which categories, directly from the graph by using link analysis techniques. The link analysis techniques are based on well-established graph mining techniques used in diverse applications such as WWW, bioinformatics, and social networks. The techniques operate directly on the patterns of connections between features in the graph rather than on statistical properties, e.g., from clustering in feature space. We argue that the resulting techniques are simpler, and we show that they perform similarly or better compared to state of the art techniques on common data sets. We also show results on more challenging data sets than those that have been used in prior work on unsupervised modeling.
Starting Page	1
Ending Page	8
File Size	1642480
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587502
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information analysis Complex networks World Wide Web Social network services Data mining Computer science Large-scale systems Bioinformatics Visual perception Web search
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Unsupervised modeling of object categories using link analysis techniques (2008)

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