NDLI: Semantic Image Segmentation Method with Multiple Adjacency Trees and Multiscale Features

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Semantic Image Segmentation Method with Multiple Adjacency Trees and Multiscale Features

Content Provider	Springer Nature Link
Author	Xie, Jun Yu, Lu Zhu, Lei Chen, Xiaohong
Copyright Year	2016
Abstract	Semantic image segmentation is the basis of image understanding, which is one of the most important human cognitive activities. Cognitive studies have shown that human neocortical information transmission depends on cognitive processing at multiple scales, and contextual information aids the human cognitive system in solving perceptual inference tasks. Inspired by multiscale cognitive mechanisms and contextual effects, in this paper, we propose a semantic image segmentation method addressing multiscale features and contextual information. To integrate multiscale features, after over-segmenting an image into small-scale segments, we employ a segment-based classifier and a CRF (conditional random field) model to generate large-scale regions. We then use the features of regions to train a region-based classifier. To capture context, we propose a multiple adjacency tree model where each tree represents one type of region relevance and can be generated by the adjacency graph corresponding to that relevance metric. Using the multiple tree model instead of a general graph model, we can perform exact inference with some simple assumptions and capture multiple types of regional context dependency. Experiments on the MSRC-21 and Stanford background datasets show advantages of our method over a segment-based CRF model using single-scale features. The results demonstrate the importance of multiscale features and contextual information.
Starting Page	168
Ending Page	179
Page Count	12
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	9
Issue Number	2
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2016-12-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Semantic image segmentation Context Multiscale features Adjacency tree Conditional random field Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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