NDLI: Feature selection based image clustering using local discriminant model and global integration

Content Provider	IEEE Xplore Digital Library
Author	Ahmed, N. Jalil, A. Khan, A.
Copyright Year	2011
Description	Author affiliation: Department of Computer and Information Sciences, PIEAS, Nilore, Islamabad, PK (Ahmed, N.; Jalil, A.; Khan, A.)
Abstract	It is well known that parameter optimization and dimension reduction strategies play an important role in improving the performance of clustering algorithms. Recently, Yang et at. (2011) proposed an interesting and effective local discriminant model and global integration (LDMGI) clustering algorithm for image databases. We observed this LDMGI approach suffers from the curse of dimensionality. We then show that the effectiveness of this approach could be substantially enhanced with parameter selection and dimensionality reduction approach. We thus experimentally observed the enhanced performance of LDMGI algorithm in terms of clustering accuracy (ACC) and normalized mutual information (NMI). In the first stage, the optimal values of the clustering parameters, nearest neighbours (k) and regularization parameter (λ) are computed. In the second stage, minimum Redundancy and maximum-Relevance (mRMR) technique is utilized to select discriminant image features. During simulation, mRMR based LDMGI model have given an overall 18.7% (ACC) and 11.8% (NMI) higher performance than LDMGI model.
Starting Page	13
Ending Page	18
File Size	725008
Page Count	6
File Format	PDF
ISBN	9781457706547
e-ISBN	9781457706578
DOI	10.1109/INMIC.2011.6151457
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-22
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases image clustering Clustering spectral clustering feature selection mRMR criteria
Content Type	Text
Resource Type	Article

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