NDLI: Unsupervised texture segmentation and labeling using biologically inspired features

Content Provider	IEEE Xplore Digital Library
Author	Martens, G. Poppe, C. Lambert, P. Van de Walle, R.
Copyright Year	2008
Description	Author affiliation: Dept. of Electron. & Inf. Syst., Ghent Univ., Ghent (Martens, G.; Poppe, C.; Lambert, P.; Van de Walle, R.)
Abstract	Due to the semantic gap, describing high-level semantic concepts with low-level visual features is a very challenging task. The classification of textures in scene images is intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired features for texture segmentation and an unsupervised method to link those texture features with semantic concepts. The calculation of the features is inspired by the human visual system and corresponds to cell outputs in the first stage of the visual cortex. Analogously to the processing principles of the cortex, self-organizing maps are employed for classification. The performance of the texture segmentation and labeling is evaluated on textures from the Brodatz album and on a real-life scenery image dataset. For both methods, a high percentage of pixels is correctly classified.
Starting Page	159
Ending Page	164
File Size	1435172
Page Count	6
File Format	PDF
ISBN	9781424422944
DOI	10.1109/MMSP.2008.4665067
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Gratings Training Pixel Gabor filters Labeling Visualization
Content Type	Text
Resource Type	Article

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