Title: Automatic semantic annotation by using fuzzy theory for natural images

Authors: Jian-Fang Cao; Jun-Jie Chen; Li-Chao Chen; Qing-Shan Zhao

Addresses: College of Computer & Software, Taiyuan University of Technology, Taiyuan City, China; Department of Computer Science & Technology, Xinzhou Teachers' University, Xinzhou City, China ' College of Computer & Software, Taiyuan University of Technology, Taiyuan City, China ' College of Computer Science and Technology, Taiyuan University of Science and Technology, China ' Department of Computer Science & Technology, Xinzhou Teachers' University, Xinzhou City, China

Abstract: Nowadays, more and more digital images are available. How to find a required image quickly for a user has become harder and harder. Acquiring semantic information of images and realising automatic image annotation is an effective technology to improve the performance of image retrieval. This paper presents a method of sentiment annotation of natural images based on fuzzy theory. The method describes image emotional level by computing fuzzy membership degree, uses BP neural network to implement it and solves semantic ambiguity on automatic image annotation. Using 967 natural images downloaded by Baidu photo channel to train and test, experiments achieved good effect compared with manual annotation results. The proposed method can lay a good foundation for automatic semantic annotation of more types of images.

Keywords: automatic semantic annotation; fuzzy theory; sentiment modelling; BP neural networks; fuzzy membership degree; fuzzy logic; digital images; image retrieval; natural images; emotional levels; emotion.

DOI: 10.1504/IJWMC.2013.056555

International Journal of Wireless and Mobile Computing, 2013 Vol.6 No.4, pp.384 - 391

Received: 20 May 2013
Accepted: 24 Jun 2013
Published online: 16 Oct 2014 *

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