Fuzzy Neighbor Voting for Automatic Image Annotation

[1] D. Zhang , Md. Monirul Islam, and Guo jun Lu, “A review on automatic image annotation techniques”, Pattern Recognition , vol. 45, pp. 346-362, 2012.

[2] F. Wang, “A survey on automatic image annotation and trends of the new age”, Procedia Engineering, vol. 23, pp. 434-438, 2011.

[3] R. Datta, D. Joshi, J. Li, and J. Wang, “Image retrieval: Ideas, influences, and trends of the new age”, ACM Comput. Surveys (CSUR), vol. 40, no. 2, pp. 5, 2008.

[4] Y. Liu, D. Zhang, G. Lu, and W. Ma, “survey of content-based image retrieval with high-level semantics”, Pattern Recognition, vol. 40, no. 1, pp. 262-282, 2007.

[5] Yuan. Ying, F. Wu, J. Shao, and Y. Zhuang, “Image annotation by semi-supervised cross-domain learning with group sparsity”, Journal of Visual Communication and Image Representation, vol. 24, no. 2, pp. 95-102, 2013.

[6] J. Liu, M. Li, Q. Liu, H. Lu, and S. Ma, “Image annotation via graph learning, Pattern Recognition”, vol. 42, no. 2, pp. 218- 228, Feb. 2009.

[7] Ch. Huang, F. Meng, W. Luo, and Sh. Zhu, “Bird breed classification and annotation using saliency based graphical model”, Journal of Visual Communication and Image Representation, vol. 25, no. 6, pp. 1299-1307, 2014.

[8] D. Zhang, , M. Islam, and G. Lu, “Structural image retrieval using automatic image annotation and region based inverted file”, Journal of Visual Communication and Image Representation, vol. 24, no. 7, pp. 1087-1098, 2013.

[9] Ja-Hwung Su, et al. “Effective semantic annotation by imageto-concept distribution model”, Multimedia, IEEE Transactions on, vol. 13.3, pp. 530-538, 2011.

[10] Y. Yang, Z. Huang, Y. Yang, J. Liu, H. Tao Shen, and J. Luo, “Local image tagging via graph regularized joint group sparsity”, Pattern Recognition, vol. 46, no. 5, pp. 1358-1368, 2012.

[11] X. Li, C.G.M. Snoek, and M. Worring, “Learning Social Tag Relevance by Neighbor Voting”, IEEE Trans. Multimedia, vol. 11, no. 7, pp. 1310-1322, Nov. 2009.

[12] L. Wu, R. Jin, and A. K. Jain, “Tag Completion for Image Retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 716-727, March 2013.

[13] S. Lee, W. De Neve, Y. Man Ro, “Visually weighted neighbor voting for image tag relevance learning”, Multimed Tools Appl, April, 2013. DOI 10.1007/s11042-013-1439-3.

[14] L. H. Zadeh, Fuzzy sets, Information and control, 1965.

[15] Ross, J Timothy, “Fuzzy logic with engineering applications”, John Wiley & Sons, 2009.

[16] J. A. Sanz, , M. Galar, A. Jurio, A. Brugos, M. Pagola, and H. Bustince, “Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system”, Applied Soft Computing, 2013.

[17] S. Dasiopoulou, C. Doulaverakis, V. Mezaris, I. Kompatsiaris, M.G. Strintzis, “An Ontology-Based Framework for Semantic Image Analysis and Retrieval”, Semantic-Based Visual Information Retrieval, Yu-Jin ZHANG (Eds), Idea Group Inc., 2007.

[18] Zh. Hua, X. Wang, Q. Liu, H. Lu, “Semantic knowledge extraction and annotation for web images”, Proceedings of the 13th annual ACM international conference on Multimedia, Hilton, Singapore, November 06-11, 2005.

[19] M. Han, X. Zhu, W. Yao, “Remote sensing image classification based on neural network ensemble algorithm”, Neurocomputing, vol. 78 (1), pp. 133-138, 2012.

[20] Y. Han, F. Wu, Q. Tian, Y. Zhuang “Image annotation by inputoutput structural grouping sparsity”, IEEE Transactions on Image Processing (99), 2012.

[21] Z. Chen, Zh. Chi, H. Fu, D. Feng, “Multi-instance multi-label image classification: A neural approach”, Neurocomputing, vol. 99 , pp. 298-306, 2013.

[22] Sh. Zhang, J. Huang, H. Li, and D. N. Metaxas, “Automatic image annotation and retrieval using group sparsity”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 3, pp. 838-849, 2012.

[23] T. Chaira, and A. K. Ray, “Fuzzy measures for colour image retrieval”, Fuzzy Sets and Systems , pp. 545-560 , 2005. [24] F. Long, H. Zhang, and D.D. Feng, “Fundamentals of contentbased image retrieval”, in: Multimedia Information Retrieval and Management: Technological Fundamentals and Applications, Springer, 2003.

[25] S. Jeong, C.S. Won, R.M. Gray, Image retrieval using colour histograms generated by Gauss mixture vector quantization, Comput. Vision Image Underst. vol. 94 (1–3), pp. 44-66, 2004.

[26] Y. Yang, Z. Huang, H. T. Shen, Zhou, X., “Mining multi-tag association for image tagging”, World Wide Web vol. 14(2), 133-156., 2011.

[27] P. Villar, A. Fernandez, R. A. Carrasco, and F. Herrera, “Feature selection and granularity learning in genetic fuzzy rule-based classification systems for highly imbalanced data-sets.”, International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems, vol. 20 (03),369-397, 2012.

[28] P. Duygulu, K. Barnard, J. De Freitas, and D. Forsyth, “Object recognition as machine translation:learning a lexicon for a fixed image vocabulary”, Proceedings of European Conferenceon Computer Vision(ECCV), vol. 2353, pp. 97-112., 2002.

[29] J. Huang, S. Kuamr, M. Mitra, W.-J. Zhu, R. Zabih, “Image indexing using colour correlogram”, in: Proceedings of the CVPR97, pp. 762-765., 1997.

[30] H. Yu, M. Li, H. Zhang, and J. Feng, “Color texture moment for content- based image retrieval”, in Proc. ICIP, pp. 929–932, 2002.

[31] B. S. Manjunath, and W. Y. Ma, “Texture features for browsing and retrieval of image data, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol 18, no. 8, pp. 837-842., 1996.

[32] J. Jeon, V. Lavrenko, R. Manmatha, “Automatic image annotation and retrieval using cross-media relevance models”, In: 26th annual international ACM SIGIR conference on research and development in information retrieval. ACM, Toronto, 28 July-1 August 2003, pp 119-126.

[33] V. Lavrenko, R. Manmatha, J. Jeon, “A model for learning the semantics of pictures”, In: 16th conference on advances in neural information processing systems (NIPS 16), Vancouver. MIT Press, Canada,8-13 December 2003.

[34] A. Yavlinsky, E. Schofield, and S. Ruger, “Automated image annotation using global features and robust nonparametric density estimation”, in Proc. ACM Int. Conf. Image Video Retrieval,pp. 507-517, 2005.

[35] S. Zhu, X. Tan, “A novel automatic image annotation method based on multi-instance learning”, Procedia Eng, vol. 15:3439- 3444, 2011.

[36] N. El-Bendary , T. h. Kim , A. Hassanien , M. Sami, “Automatic image annotation approach based on optimization of classes scores”, Computing, 96(5), pp. 381-402, 2014.

[37] Li, Zhixin, L. Li, K. Yan, and C. Zhang, “Automatic image annotation based on fuzzy association rule and decision tree." InProceedings of the 7th International Conference on Internet Multimedia Computing and Service, p. 12. ACM, 2015.

[38] S.L. Feng, R. Manmatha, and V. Lavrenko, “Multiple bernoulli relevance models for image and video annotation”, In Computer Vision and Pattern Recognition. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on (vol. 2, pp. II-1002). IEEE. 2004.

[39] A. Makadia, V. Pavlovic, and S. Kumar, “A new baseline for image annotation”, In Computer VisionECCV 2008 (pp. 316- 329). Springer Berlin Heidelberg. 2008.

[40] D. Arias-Aranda, J. L. Castro, M. Navarro, J. M. Sánchez, and J. M. Zurita, “A fuzzy expert system for business management”, Expert Systems with Applications 37, no. 12 (2010): 7570-7580.

[41] V. Maihami, F. Yaghmaee, “Color Features and Color Spaces Applications to the Automatic Image Annotation”, Book chapter in Emerging Technologies in Intelligent Applications for Image and Video Processing. 2016 Jan 7:378.

[42] J. Johnson, L. Ballan, and L. Fei-Fei, “Love thy neighbors: Image annotation by exploiting image metadata”. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4624-4632), 2015.

[43] X. Li, T. Uricchio, L. Ballan, M. Bertini, CG. Snoek, A. Del Bimbo, “Socializing the semantic gap: A comparative survey on image tag assignment, refinement and retrieval”, ACM Computing Surveys. arXiv preprint arXiv:1503.08248. 2016.