Document Type : Original Research Paper

Authors

Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran

Abstract

With quick development of digital images and the availability of imaging tools, massive amounts of images are created. Therefore, efficient management and suitable retrieval, especially by computers, is one of the
most challenging fields in image processing. Automatic image annotation (AIA) or refers to attaching words, keywords or comments to an image or to a selected part of it. In this paper, we propose a novel image annotation algorithm based on neighbor voting which uses fuzzy system. The performance of the model depends on selecting the right neighbors and a fuzzy system with the right combination of features it offers.
Experimental results on Corel5k and IAPR TC12 benchmark annotated datasets, demonstrate that using the proposed method leads to good performance.

Graphical Abstract

Fuzzy Neighbor Voting for Automatic Image Annotation

Keywords

[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.

LETTERS TO EDITOR

Journal of Electrical and Computer Engineering Innovations (JECEI) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in JECEI should be sent to the editorial office of JECEI within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.


[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

CAPTCHA Image