By Zongmin Ma
The expanding pattern of multimedia information use is probably going to speed up developing an pressing desire of supplying a transparent technique of shooting, storing, indexing, retrieving, interpreting, and summarizing information via picture info.
Artificial Intelligence for Maximizing content material dependent photograph Retrieval discusses significant facets of content-based photo retrieval (CBIR) utilizing present applied sciences and functions in the synthetic intelligence (AI) box. offering state of the art examine from prime overseas specialists, this ebook bargains a theoretical point of view and useful options for academicians, researchers, and practitioners.
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Extra resources for Artificial intelligence for maximizing content based image retrieval
Color image processing: Methods and applications. CRC Press Korolev, L. N. (2007). On evolutionary algorithms, neural-network computations, and genetic programming. Mathematical problems. International Journal on Automation and Remote Control, 68(5), 811-821. Plenum Press. , & Shi, Z. (2004). Semantic feature extraction using genetic programming in image retrieval. Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, 1(23-26), pp. 648-651. IEEE Computer Society Press.
This last process is motivated by the hope that the new population will be better than the old one. In each generation, the fitness of each candidate solution is evaluated, multiple candidate solutions are stochastically selected from the current solutions (based on their fitness), and modified (recombined and/or mutated and/or others “genetic” operations) to form a new population of candidate solutions. The new population is then used in the next iteration of the algorithm. The GAs, adopted with this paradigm, are generally used to perform optimization tasks in several different application domains.
The achieved results demonstrate that the classification of images is extremely fast and accurate. INTRODUCT Since the early 1990’s, there has been considerable research carried out into content-based image retrieval (CBIR) systems. A few systems have been installed commercially, including Query-By-Image-Content (QBIC) (Niblack, Barber, Equitz, Flickner, Glasman, Petkovic, Yanker, Faloutsos, and Taubin, 1993), the VIR Image Engine (Bach, Fuller, Gupta, Hampapur, Gorowitz, Humphrey, Jain, and Shu, 1996), the AltaVista Photofinder, Multimedia Analysis and Retrieval System (MARS) (Huang, Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.