NDLI: Adaptive image classification based on folksonomy

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Adaptive image classification based on folksonomy

Content Provider	IEEE Xplore Digital Library
Author	Guldogan, E. Gabbouj, M.
Copyright Year	2010
Description	Author affiliation: Department of Signal Processing, Tampere University of Technology, Finland (Guldogan, E.; Gabbouj, M.)
Abstract	In this paper, we present a novel adaptive image classification method for content-based image classification systems based on user defined tags and annotations. The proposed method utilizes low-level features and folksonomies for improved classification accuracy. Thus, users' perceptive semantics are modeled in terms of low-level features and they are combined with low-level image categorization adaptively. The proposed method has been thoroughly evaluated and selected results are illustrated in the paper. It is shown that, satisfactory improvements can be achieved with integrating folksonomies into classification scheme. Furthermore, it is a language-independent and low-complex method that can be used on various databases, languages and Content-Based Image Retrieval applications.
Starting Page	1
Ending Page	4
File Size	675032
Page Count	4
File Format	PDF
ISBN	9781424478484
e-ISBN	9788890532801
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-12
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Univ of Brescia
Subject Keyword	Databases Poles and towers Semantics Humans Classification algorithms
Content Type	Text
Resource Type	Article

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