Using neuro-fuzzy techniques based on a two-stage mapping model for concept-based image database indexing

We present a two-stage mapping model (TSMM), which is intended to minimise the semantic gap for content-based image retrieval (CBIR) by reducing recognition errors during the image indexing stage. This model is composed of a feature extraction module based on our image segmentation and feature extraction algorithm, a colour and texture classification modules based on support vector machines (SVMs), and an inference module based on fuzzy logic to make final decisions as high level concepts from the colour and texture concepts. The experimental results show that the proposed method outperforms general approaches by using one single SVM classifier as direct mapping between the combined colour and texture feature vectors and high level concepts directly.

[1]  J. Revelli,et al.  Book Rvw, The Image Processing Handbook, 4th Edition , by John Russ , 2003 .

[2]  Chih-Fong Tsai.  Stacked generalisation: a novel solution to bridge the semantic gap for content-based image retrieval , 2003, Online Inf. Rev..

[3]  Ludmila I. Kuncheva,et al.  Fuzzy Classifier Design , 2000, Studies in Fuzziness and Soft Computing.

[4]  S. Pizer,et al.  The Image Processing Handbook , 1994 .

[5]  Masao Sakauchi,et al.  Discussing Web Pages with Chat-Pointers in E-coBrowse , 2004, Multimedia Tools and Applications.

[6]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[7]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

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

[9]  Yi-Ping Hung,et al.  Future mulitmedia databases and research directions , 2002 .

[10]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[11]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[12]  Joseph Revelli,et al.  The Image Processing Handbook, 4th Edition , 2003, J. Electronic Imaging.

[13]  Alberto Del Bimbo,et al.  Visual information retrieval , 1999 .

[14]  John Tait,et al.  Image classification using hybrid neural networks , 2003, SIGIR.

[15]  Chih-Jen Lin,et al.  On the convergence of the decomposition method for support vector machines , 2001, IEEE Trans. Neural Networks.

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  Arivazhagan Selvaraj,et al.  Texture classification using wavelet transform , 2003, Pattern Recognit. Lett..

[18]  Masafumi Hagiwara,et al.  An image retrieval system by impression words and specific object names - IRIS , 2002, Neurocomputing.

[19]  Ramesh C. Jain,et al.  A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video , 2002, Pattern Recognit..

[20]  Gerard Salton,et al.  Research and Development in Information Retrieval , 1982, Lecture Notes in Computer Science.

[21]  Thomas S. Huang,et al.  Unifying Keywords and Visual Contents in Image Retrieval , 2002, IEEE Multim..

[22]  John R. Smith,et al.  New frontiers for intelligent content-based retrieval , 2001, IS&T/SPIE Electronic Imaging.

[23]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[24]  Amanda J. C. Sharkey,et al.  Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems , 1999 .

[25]  John Tait,et al.  Search strategies in content-based image retrieval , 2003, SIGIR.

[26]  Martin Szummer,et al.  Indoor-outdoor image classification , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[27]  James Ze Wang,et al.  Learning-based linguistic indexing of pictures with 2--d MHMMs , 2002, MULTIMEDIA '02.