An Adaboost-Backpropagation Neural Network for Automated Image Sentiment ClassificationReport as inadecuate

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The Scientific World Journal - Volume 2014 2014, Article ID 364649, 9 pages -

Research Article

School of Computer Science & Technology, Taiyuan University of Technology, Taiyuan 030024, China

Department of Computer Science & Technology, Xinzhou Teachers’ University, No. 10 Heping West Street, Xinzhou 034000, China

Received 2 May 2014; Revised 2 July 2014; Accepted 10 July 2014; Published 4 August 2014

Academic Editor: Chengcui Zhang

Copyright © 2014 Jianfang Cao et al. 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.


The development of multimedia technology and the popularisation of image capture devices have resulted in the rapid growth of digital images. The reliance on advanced technology to extract and automatically classify the emotional semantics implicit in images has become a critical problem. We proposed an emotional semantic classification method for images based on the Adaboost-backpropagation BP neural network, using natural scenery images as examples. We described image emotions using the Ortony, Clore, and Collins emotion model and constructed a strong classifier by integrating 15 outputs of a BP neural network based on the Adaboost algorithm. The objective of the study was to improve the efficiency of emotional image classification. Using 600 natural scenery images downloaded from the Baidu photo channel to train and test the model, our experiments achieved results superior to the results obtained using the BP neural network method. The accuracy rate increased by approximately 15% compared with the method previously reported in the literature. The proposed method provides a foundation for the development of additional automatic sentiment image classification methods and demonstrates practical value.

Author: Jianfang Cao, Junjie Chen, and Haifang Li



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