NDLI: Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field

Content Provider	Springer Nature Link
Author	Kim, Changick
Copyright Year	2015
Abstract	In analyzing natural scene images, texture plays an important role because such images are full of various textures. Although texture is crucial information in analyzing natural scene images, the texture segmentation problem is still hard to solve since the texture often exhibit non-uniform statistical characteristics. Although there are several supervised approaches that partition an image according to pre-defined semantic categories, the ever-changing appearances in the natural images make such schemes intractable. To overcome this limitation, we propose a novel unsupervised texture segmentation method for natural images by using the Region-based Markov Random Field (RMRF) model which enforces the spatial coherence between neighbor regions. We introduce the concept of pivot regions which plays a decisive role to incorporate local data interaction. By forcing pivot regions to adhere to initial labels, we make the Markov Random Field evolve fast and precisely. The proposed algorithm based on the pivot regions and the MRF for encapsulating spatial dependencies between neighborhoods yields high performance for the unsupervised segmentation of natural scene images. Quantitative and qualitative evaluations prove that the proposed method achieves comparable results with other algorithms.
Starting Page	423
Ending Page	436
Page Count	14
File Format	PDF
ISSN	19398018
Journal	Journal of Signal Processing Systems
Volume Number	83
Issue Number	3
e-ISSN	19398115
Language	English
Publisher	Springer US
Publisher Date	2015-09-05
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Unsupervised texture segmentation Markov random field Natural scene images Signal, Image and Speech Processing Circuits and Systems Electrical Engineering Image Processing and Computer Vision Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Signal Processing Control and Systems Engineering Information Systems Modeling and Simulation Hardware and Architecture

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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