Abstract
This paper proposes a neuro-fuzzy based CBIR framework for image retrieval. Here, in the first phase, the fuzzy clustering algorithm is used for the classification of the images on the basis of their texture feature. In second phase, result of first phase serves as an input to the back propagation algorithm which helps to find images most semantically related to the query images. Our experiment shows that the proposed method results in better performance in terms of precision and recall as compared to the traditional CBIR techniques.
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References
Saad, M.: Content-Based Image Retrieval, A Literature Survey (2008)
Datta, R., Li, J., Wang, J.: Content-based image retrieval—a survey on the approaches and trends of the new age, ACM multimedia. In: Proceedings of International Retrieval 2005
Tamura, H., et.al., Textural Features Corresponding to Visual Perception, Transactions on Systems, man, and cybernetics (IEEE), vol. SMC—8, No. 6 (1978)
Thompson, W.B.: Textural Boundary Analysis, Transactions on computers (IEEE) (1997)
Kulkarni, S.: Natural language based fuzzy queries and fuzzy mapping of feature database for image retrieval. J. Inf. Technol. Appl. 4(1), 11–20 (2010)
Rama Krishna Rao, T.K., Usha Rani, R.: Content based image retrieval through feed forward neural networks. In: IEEE 8th International Colloquium on Signal Processing and its Applications (2012)
Verma, B., Kulkarni, S.: Fuzzy logic based Texture Queries for CBIR. In: IEEE proceedings of the fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’03) (2003)
Lin, H.-C., Chiu, C.-Y., Yang, S.-N.: Finding textures by textual descriptions, visual examples and relevance feedbacks. Pattern Recogn. Lett. (2003)
Ahmed, H.A., Onsi, H., Gayar, N.E.: A New Approach in Content- Based Image Retrieval using Fuzzy Logic, INFOS2008, Cairo-Egypt (2008)
Medasani, S., Krishnapuram, R.: A fuzzy approach to content-based image retrieval. In: IEEE International Fuzzy Systems Conference Proceedings, Seoul, Korea, 22–25 Aug 1999
Borse, R., et.al. Efficient implementation of CBIR system and framework of fuzzy semantics. In: Proceedings of IEEE International Conference on Advances in Mobile Network Communication and its Applications (2012)
Fazal-e-Malik, Baharudin, B.: Efficient Image Retrieval Based on Texture Features (IEEE) (2011)
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Bansal, A.K., Swati Mathur (2016). CBIR Feature Extraction Using Neuro-Fuzzy Approach. In: Afzalpulkar, N., Srivastava, V., Singh, G., Bhatnagar, D. (eds) Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2638-3_60
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DOI: https://doi.org/10.1007/978-81-322-2638-3_60
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