Neural Network approach for image retrieval based on preference elicitation

Multimedia technologies have been developing rapidly over the last few years and have yielded a large number of databases containing graphical documents. Tools for content-based search of graphical objects have been the subject of intensive research, but their performance is still unsatisfactory for many applications, opening up afield for further research and technology development. Up till now, all popular Internet search engines have been only text-based, including those that search for images. In this paper We propose an image retrieval system based on neural networks. The advantage of using the neural network is that the amount of semantic gap can be reduced when compared to other techniques which may be based on clustering. The methodology proposed below is designed for a specific class of objects, which can be broken down into subobjects in such a way that the main object can be classified by shape, color distribution and texture of the sub objects and the spatial spatial relations between the sub-objects in a 2dimensional image. We also assume that translation, scaling and 2D rotation do not change the class of the object, but we do not consider 3Dtransformation.Therefore, photos of the same 3D object from different positions for example are considered to be objects belonging to different class keywords: Neural network; relevance feedback; semantic gap

[1]  Francis J. Narcowich,et al.  A First Course in Wavelets with Fourier Analysis , 2001 .

[2]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[3]  Amarnath Gupta,et al.  Virage image search engine: an open framework for image management , 1996, Electronic Imaging.

[4]  Nicolaos B. Karayiannis,et al.  Reformulated radial basis neural networks trained by gradient descent , 1999, IEEE Trans. Neural Networks.

[5]  Nikolaos D. Doulamis,et al.  Evaluation of relevance feedback schemes in content-based in retrieval systems , 2006, Signal Process. Image Commun..

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

[7]  Bing Yu,et al.  Training radial basis function networks with differential evolution , 2006, 2006 IEEE International Conference on Granular Computing.

[8]  Hsiang-Cheh Huang,et al.  A Multiple-Instance Neural Networks based Image Content Retrieval System , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[9]  Yu Liu,et al.  Training Radial Basis Function Networks with Particle Swarms , 2004, ISNN.

[10]  André da Motta Salles Barreto,et al.  Growing compact RBF networks using a genetic algorithm , 2002, VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings..

[11]  Dan Simon,et al.  Training radial basis neural networks with the extended Kalman filter , 2002, Neurocomputing.