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By Zongmin Ma

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

Artificial Intelligence for Maximizing content material dependent picture Retrieval discusses significant points of content-based snapshot retrieval (CBIR) utilizing present applied sciences and purposes in the man made intelligence (AI) box. delivering state of the art learn from top overseas specialists, this e-book bargains a theoretical viewpoint and sensible strategies for academicians, researchers, and practitioners.

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Artificial intelligence for maximizing content based image retrieval

The expanding development of multimedia facts use is probably going to speed up growing an pressing want of supplying a transparent technique of shooting, storing, indexing, retrieving, studying, and summarizing information via photo facts. synthetic Intelligence for Maximizing content material dependent photograph Retrieval discusses significant points of content-based picture retrieval (CBIR) utilizing present applied sciences and functions in the man made intelligence (AI) box.

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Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-110. , Brumby, S. , Harvey, N. , Szymanski, J. , & Bloch, J. J. (2000). GENIE - A hybrid genetic algorithm for feature classification in multi-spectral images. Los Alamos National Laboratory Internal Proceeding, SPIE 4120 (pp. 52-62). , & Theiler, J. (2003). Weighted order statistic classifiers with large rankorder margin. Proceedings of the Twentieth International Conference on Machine Learning, ICML 20, 600-607.

They are based on probabilistic graphical models. In the graph nodes represent random variables and arcs represent dependencies between random variables with conditional probabilities at nodes. Frequently, BNNs are used to obtain many advantages over traditional methods of determining causal relationships (Limin, 2006). For example, the BNNs parameters of the models are expressed as a probability distribution rather than a single set of values. Other advantages regard the possibility (by the model) to support a powerful supervised ate learning phase and the opportunity to have a well knowledge feedback mechanisms to ensure an acceptable range of errors.

References Arunkumar, S. (2004). 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).

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