NDLI: Fusing cortex transform and intensity based features for image texture classification

Content Provider	IEEE Xplore Digital Library
Author	Khayrul Bashar, Md. Ohnishi, N.
Copyright Year	2002
Description	Author affiliation: Dept. of Inf. Eng., Nagoya Univ., Japan (Khayrul Bashar, Md.; Ohnishi, N.)
Abstract	This paper proposes a new scheme of fusing cortex transform and brightness based features obtained by local windowing operation. Energy features are obtained by applying popular cortex transform technique within a sliding window rather than the conventional way, while we define three features namely directional surface density (DSD), normalised sharpness index (NSI), and normalized frequency index (NFI) as measures for pixel brightness variation. Fusion by simply vector tagging as well as by correlation is performed in the feature space and then classification is done using minimum distance classifier on the fused vectors. It is interesting that the brightness features, though inferior on some natural images, often produces smoother texture boundary in mosaic images, whereas energy features show the opposite behavior. This symmetrically inverse property is combined through vector fusion for robust classification of multi-texture images obtained from Brodatz album and VisTex database. Classification outcome with confusion matrix analysis shows the robustness of the scheme.
Starting Page	1463
Ending Page	1469
File Size	714033
Page Count	7
File Format	PDF
ISBN	0972184414
DOI	10.1109/ICIF.2002.1020988
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-07-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Int. Soc. of Information Fusion
Subject Keyword	Image texture Brightness Robustness Density measurement Energy measurement Frequency measurement Tagging Image databases Spatial databases Symmetric matrices
Content Type	Text
Resource Type	Article

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