NDLI: Bayesian network based semantic image classification with attributed relational graph

Content Provider	Springer Nature Link
Author	Ri, Chang Yong Yao, Min
Copyright Year	2014
Abstract	Semantic image classification is a hot issue of image mining. Information of spatial relations between objects in an image is one of the important semantic information of an image. However, the previous researches have not made full use of the spatial relations for image modeling and classification. In addition, to classify the images with Bayesian network, the accuracy of conditional probability estimation may be insufficient, because the learning methods of spatial contextual models have usually used a limited number of training samples. In this work, the semantic image modeling based on attributed relational graph has been proposed, in which the distance measure method between images was presented, therefore the object information and spatial relational information could be fully utilized. Then, the semantic distance between images based on attributed relational graph could be calculated for the support vector machine to obtain the joint conditional probability distribution of Bayesian network. Therefore the probabilistic estimation problem under the sparse training samples could be solved, and the accuracy of semantic image classification with Bayesian network was improved. Experimental results show the validity and reliability of this proposed method.
Starting Page	4965
Ending Page	4986
Page Count	22
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	74
Issue Number	13
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2014-02-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Semantic image classification Attributed relational graph Semantic distance Bayesian network Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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