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Computational Intelligence and Neuroscience
Volume 2015, Article ID 971039, 10 pages
http://dx.doi.org/10.1155/2015/971039
Research Article

Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

1Department of Computer Science & Technology, Xinzhou Teachers University, Xinzhou 034000, China
2School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China

Received 16 September 2014; Revised 4 February 2015; Accepted 22 February 2015

Academic Editor: Cheng-Jian Lin

Copyright © 2015 Jianfang Cao and Lichao Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance.