NDLI: Image Clustering Using Visual and Text Keywords

Content Provider	IEEE Xplore Digital Library
Author	Agrawal, R. Changhua Wu Grosky, W.I. Fotouhi, F.
Copyright Year	2007
Description	Author affiliation: Kettering Univ., Flint (Agrawal, R.; Changhua Wu)
Abstract	In classical image classification approaches, low-level features have been used. But the high dimensionality of feature spaces poses a challenge in terms of feature selection and distance measurement during the clustering process. In this paper, we propose an approach to generate visual keyword and combine both visual and text keywords of the image to form a multimodal vector for image classification. This multimodality helps in extracting the image to image, text to text and text to image relations. A visual keyword is derived using vector quantization of image tiles. We arrange the visual keywords in a manner analogous to the term-document matrix in information retrieval. The visual keywords when combined with text keywords result in improvement in the quality of classification. We use a recently proposed nonlinear dimensionality reduction technique, diffusion maps, to reduce the dimensionality of the image representation. Our method is evaluated on two public datasets: LabelMe and Corel. The results support the conclusion that the proposed method of combining visual and text keywords is robust and produces good quality clusters.
Starting Page	49
Ending Page	54
File Size	406969
Page Count	6
File Format	PDF
ISBN	1424407893
DOI	10.1109/CIRA.2007.382923
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vector quantization Principal component analysis Image classification Computational intelligence Robotics and automation USA Councils Frequency Laplace equations Graph theory Extraterrestrial measurements
Content Type	Text

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