NDLI: Supervised texture segmentation using localized dictionary based data modelling

Content Provider	IEEE Xplore Digital Library
Author	Ranjan, R. Gupta, S. Venkatesh, K.S.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Indian Inst. of Technol. Kanpur, Kanpur, India (Ranjan, R.; Gupta, S.; Venkatesh, K.S.)
Abstract	In this paper, we propose a supervised algorithm for texture segmentation that uses sparsity based localized data modelling. The problem addressed is to segment a given test image whose constituent textures are known a priori. Overlapping patches are extracted from the texture. Each texture is modelled by learning the patterns of the patches that constitutes training data set. For each set of training data, a set of dictionaries are learnt, contrary to the conventional practice of one dictionary for all the patches of a texture. Each dictionary is learnt to capture the local pattern in the texture data. Texture is modelled by two level pattern learning. At the first level, clustering is used to learn the macro variations in the data pattern. Subsequently, data pattern in every cluster is modelled by a sparsity based subspace learning. These are subspaces where actual texture data lie. The set of subspaces are captured by learning a dictionary. The advantage of this approach is the accurate modelling of local data patterns which a conventional single dictionary is incapable of. Simulation results validate the proposed claim by achieving higher segmentation accuracy.
Starting Page	275
Ending Page	279
File Size	1108216
Page Count	5
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900670
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Dictionaries Image segmentation Signal processing algorithms Data models Accuracy Digital signal processing OMP Supervised Texture Segmentation Multi-level Pattern Learning Sparse Framework K-SVD
Content Type	Text
Resource Type	Article

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