Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 364649, 9 pages
Research Article

An Adaboost-Backpropagation Neural Network for Automated Image Sentiment Classification

1School of Computer Science & Technology, Taiyuan University of Technology, Taiyuan 030024, China
2Department 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.

Linked References

  1. Y. Shin, Y. Kim, and E. Y. Kim, “Automatic textile image annotation by predicting emotional concepts from visual features,” Image and Vision Computing, vol. 28, no. 3, pp. 526–537, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. X. Mao, Y.-K. Ding, and M. Itsuya, “Analysis of affective characteristics and evaluation on harmonious feeling of image,” Chinese Journal of Electronics, vol. 29, pp. 1923–1927, 2001. View at Google Scholar
  3. P.-T. Li, Y.-X. Shi, and H.-G. Dai, “Classification of house-designing image based on color feature,” Computer Engineering, vol. 37, pp. 224–226, 2011. View at Google Scholar
  4. S.-F. Wang, E. Chen, S.-H. Wang, and X.-F. Wang, “Emotion model-based perceptual image retrieval,” Journal of Circuits and Systems, vol. 8, pp. 48–52, 2003. View at Google Scholar
  5. H. Li, H. He, and J. Chen, “A multi-layer affective model based on personality, mood and emotion,” Journal of Computer-Aided Design and Computer Graphics, vol. 23, no. 4, pp. 725–730, 2011. View at Google Scholar · View at Scopus
  6. S. Cho and J. Lee, “A human-oriented image retrieval system using interactive genetic algorithm,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., vol. 32, no. 3, pp. 452–458, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. Q.-Z. Liu, L. Zhang, and J.-J. Wang, “Support vector machine-based image emotion classification,” Journal of Dalian University, vol. 29, pp. 47–51, 2008. View at Google Scholar
  8. Y. Liu, X. Chen, C. Zhang, and A. Sprague, “Semantic clustering for region-based image retrieval,” Journal of Visual Communication and Image Representation, vol. 20, no. 2, pp. 157–166, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Ortony, G. L. Clore, and A. Collins, The Cognitive Structure of Emotions, Cambridge University Press, Cambridge, UK, 1988.
  10. J.-J. Chen, H.-F. Li, J. Xiang, and J.-J. Zhao, Emotional Analysis, Publishing House of Electronics Industry, Beijing, China, 2011.
  11. G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos, “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 394–410, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. V. Dey, Y. Zhang, and M. Zhong, “A review on image segmentation techniques with remote sensing perspective,” in Proceedings of the International Society for Photogram metry and Remote Sensing Symposium, Vienna, Austria, July 2010.
  13. D. Zhang, M. M. Islam, G. Lu, and J. Hou, “Semantic image retrieval using region based inverted file,” in Proceedings of the Digital Image Computing: Techniques and Applications (DICTA '09), pp. 242–249, Melbourne, Australia, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Zhang, X. Zheng, and W. Lei, “Method of color image retrieval based on quantified color space,” Computer Simulation, vol. 27, pp. 194–196, 2010. View at Google Scholar
  15. H.-K. Wei, Theory and Methods of Neural Network Structure Design, National Defence Industry Press, Beijing, China, 2005.
  16. X.-Y. Zhong and J. Ling, “Adaboost detector based on multiple thresholds for weak classifier,” Computer Engineering and Applications, vol. 45, pp. 160–162, 2009. View at Google Scholar
  17. J. Machajdik and A. Hanbury, “Affective image classification using features inspired by psychology and art theory,” in Proceedings of the 18th ACM International Conference on Multimedia ACM Multimedia, pp. 83–92, October 2010. View at Publisher · View at Google Scholar · View at Scopus