Neuro-Fuzzy Based Clustering Approach For Content Based Image Retrieval Using 2D- Wavelet Transform

In this paper we introduce neuro-fuzzy based clustering approach for content based image retrieval using 2D-wavelet transform(2D-DWT). Most of the image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity.To address this problem, we developed neural network -fuzzy logic cluster based approach for content based image retrieval using 2D-wavelet transform. The system performance improved by the learning and searching capability of the neural network combined with the fuzzy interpretation. This overcomes the vagueness and inconsistency due to human subjectivity. Multiresolution analysis using 2D-DWT can decompose the image into components at different scales, so that the coarest scale components carry the global approximation information while the finer scale components contain the detailed information. The empirical results show that the precision improved from 78% to 98% and average recall rate of 77% to 98% for the general purpose database size of 10000 images compared with other existing approaches.

[1]  Keshab K. Parhi,et al.  Combining neural networks and the wavelet transform for image compression , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Clement H. C. Leung,et al.  A Fuzzy Expert System for Concept-Based Image Indexing and Retrieval , 2005, 11th International Multimedia Modelling Conference.

[3]  Sunanda Mitra,et al.  Efficient coding by neuro-fuzzy clustering in vector quantization of wavelet decomposed signals , 1996, Proceedings of North American Fuzzy Information Processing.

[4]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  J. Jiang.  Design of a LVQ neural network for compressed image indexing , 1997 .

[6]  J. Jiang.  Neural network technology for image compression , 1995 .

[7]  Chih-Yi Chiu,et al.  Finding textures by textual descriptions, visual examples, and relevance feedbacks , 2003, Pattern Recognit. Lett..

[8]  Zhongfei Zhang,et al.  A clustering based approach to efficient image retrieval , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[9]  P. Sharma,et al.  Content based image retrieval using a neuro-fuzzy technique , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[10]  B. Solaiman,et al.  Venous Thrombosis Supervised Image Indexing and Fuzzy Retrieval , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[13]  Ling Guan,et al.  Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture , 2002, IEEE Trans. Neural Networks.

[14]  Brijesh Verma,et al.  Fuzzy logic based texture queries for CBIR , 2003, Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003.

[15]  Haifeng Zhao,et al.  An Adaptive Fuzzy Clustering Method for Query-by-Multiple-Example Image Retrieval , 2007, 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System.

[16]  Thomas S. Huang,et al.  Supporting Ranked Boolean Similarity Queries in MARS , 1998, IEEE Trans. Knowl. Data Eng..

[17]  P. Muneesawang,et al.  A neural network approach for learning image similarity in adaptive CBIR , 2001, 2001 IEEE Fourth Workshop on Multimedia Signal Processing (Cat. No.01TH8564).

[18]  Raj Acharya,et al.  Color clustering techniques for color-content-based image retrieval from image databases , 1997, Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[19]  Ronald Fagin,et al.  Combining Fuzzy Information from Multiple Systems , 1999, J. Comput. Syst. Sci..

[20]  Sunanda Mitra,et al.  Identification of noise outliers in clustering by a fuzzy neural network , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.