Storage Retrieval

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By Zongmin Ma

The expanding development of multimedia info use is probably going to speed up growing an pressing want of supplying a transparent technique of shooting, storing, indexing, retrieving, examining, and summarizing information via snapshot info.

Artificial Intelligence for Maximizing content material dependent snapshot Retrieval discusses significant facets of content-based picture retrieval (CBIR) utilizing present applied sciences and functions in the synthetic intelligence (AI) box. supplying cutting-edge learn from top overseas specialists, this ebook bargains a theoretical point of view and useful recommendations for academicians, researchers, and practitioners.

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2652 (pp. 1099-1107). Springer Berlin / Heidelberg Publisher. 19 20 Chapter II Improving Image Retrieval by Clustering Dany Gebara University of Calgary, Canada Reda Alhajj University of Calgary, Canada Abstract This chapter presents a novel approach for content-fbased image retrieval and demonstrates its applicability on non-texture images. The process starts by extracting a feature vector for each image; wavelets are employed in the process. Then the images (each represented by its feature vector) are classified into groups by employing a density-based clustering approach, namely OPTICS.

Rep No. 1). National Research Centre, Computer Science and Knowledge Laboratory (CSK Lab). , Salam, R. , & Zainol, Z. (2006). Face feature extraction using Bayesian network. Proceedings of the 4th International Conference on Computer Graphics and Interactive Techniques (pp. 261-264). ACM Press. , & Milano, L. (2005). A novel information geometric approach to variable selection in MLP Networks. International Journal on Neural Network, 18(10), 1309-1318, Elsevier Science Publishing. Freitas, A. A.

Neural network and its application in pattern recognition (Dissemination Report). Department of Computer Science and Engineering, Indian Institute of Technology, Bombay. Belkhatir, M. (2005). A symbolic query-by-example framework for the image retrieval signal/semantic integration. In Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI (pp. 348-355). Washington, DC, IEEE Computer Society Press. , & Mulhem, P. (2005). A signal/semantic framework for image retrieval.

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