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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 617618, 7 pages
http://dx.doi.org/10.1155/2013/617618
Research Article

Recursive Neural Networks Based on PSO for Image Parsing

School of Sciences, Jimei University, Xiamen, China

Received 24 February 2013; Accepted 3 March 2013

Academic Editor: Zhenkun Huang

Copyright © 2013 Guo-Rong Cai and Shui-Li 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.

Linked References

  1. C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,” IEEE Transactions on Pattern Analysis and Machine Learning, no. 99, pp. 1–15, 2012.
  2. C. Liu, J. Yuen, and A. Torralba, “Nonparametric scene parsing via label transfer,” IEEE Transactions on Pattern Analysis and Machine Learning, vol. 33, no. 12, pp. 2368–2382, 2011. View at Publisher · View at Google Scholar
  3. Z. Tu and S. C. Zhu, “Parsing images into regions, curves, and curve groups,” International Journal of Computer Vision, vol. 69, no. 2, pp. 223–249, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Porway, Q. Wang, and S. C. Zhu, “A hierarchical and contextual model for aerial image parsing,” International Journal of Computer Vision, vol. 88, no. 2, pp. 254–283, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  5. O. Teboul, L. Kokkinos, L. Simon, P. Koutsourakis, and N. Paragios, “Parsing facades with shape grammars and reinforcement learning,” IEEE Transactions on Pattern Analysis and Machine Learning. In press.
  6. A. Oliva and A. Torralba, “Modeling the shape of the scene: a holistic representation of the spatial envelope,” International Journal of Computer Vision, vol. 42, no. 3, pp. 145–175, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  7. S. Nowozin, P. V. Gehler, and C. H. Lampert, “On parameter learning in CRF-based approaches to object class image segmentation,” Lecture Notes in Computer Science, vol. 6316, no. 6, pp. 98–111, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Tighe and S. Lazebnik, “Superparsing: scalable nonparametric image parsing with superpixels,” Lecture Notes in Computer Science, vol. 6315, no. 5, pp. 352–365, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. X. He and R. Zemel, “Learning hybrid models for image annotation with partially labeled data,” in Proceeding of Advances in Neural Information Processing Systems 21, Proceedings of the 22nd Annual Conference on Neural Information Processing Systems, pp. 625–632, Vancouver, British Columbia, Canada, December 2008.
  10. S. Gould, R. Fulton, and D. Koller, “Decomposing a scene into geometric and semantically consistent regions,” in Proceedings of the 12th International Conference on Computer Vision (ICCV '09), pp. 1–8, Kyoto, Japan, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Zhu, Y. Chen, Y. Lin, C. Lin, and A. Yuille, “Recursive segmentation and recognition templates for image parsing,” IEEE Transactions on Pattern Analysis and Machine Learning, vol. 34, no. 2, pp. 359–371, 2012. View at Publisher · View at Google Scholar
  12. L. Zhu, Y. Chen, and A. Yuille, “Learning a hierarchical deformable template for rapid deformable object parsing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1029–1043, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Han and S. C. Zhu, “Bottom-up/top-down image parsing with attribute grammar,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 1, pp. 59–73, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Ladický, P. Sturgess, C. Russell et al., “Joint optimization for object class segmentation and dense stereo reconstruction,” International Journal of Computer Vision, vol. 100, no. 2, pp. 122–133, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  15. R. Socher and L. Fei-Fei, “Connecting modalities: semi-supervised segmentation and annotation of images using unaligned text corpora,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 966–973, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. K. Huang, C. H. Feng, and S. Mohamad, “Multistability analysis for a general class of delayed Cohen-Grossberg neural networks,” Information Sciences, vol. 187, pp. 233–244, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  17. R. Socher, C. C. Lin, A. Y. Ng, and C. D. Manning, “Parsing natural scenes and natural language with recursive neural networks,” in Proceedings of the International Conference on Machine Learning, pp. 129–136, 2011. View at Publisher · View at Google Scholar
  18. J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia, December 1995.
  19. G. R. Cai, S. Z. Li, S. L. Chen, and Y. D. Wu, “A fuzzy neural network model of linguistic dynamic systems based on computing with words,” Journal of Donghua University, vol. 27, no. 6, pp. 813–818, 2010. View at Scopus
  20. D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Learning, vol. 24, no. 5, pp. 603–619, 2002. View at Publisher · View at Google Scholar
  21. Y. Shi and R. Eberhart, “Parameter selection in particle swarm optimization,” in Proceeding of the 7th International Conference on Evolutionary Programming VII (EP '89), pp. 591–600, 1998. View at Publisher · View at Google Scholar