By Zongmin Ma
The expanding pattern of multimedia facts use is probably going to speed up growing an pressing want of offering a transparent technique of shooting, storing, indexing, retrieving, studying, and summarizing facts via snapshot info.
Artificial Intelligence for Maximizing content material dependent photo Retrieval discusses significant elements of content-based snapshot retrieval (CBIR) utilizing present applied sciences and functions in the man made intelligence (AI) box. delivering cutting-edge learn from top overseas specialists, this booklet bargains a theoretical point of view and sensible recommendations for academicians, researchers, and practitioners.
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Extra resources for Artificial intelligence for maximizing content based image retrieval
Improving Image Retrieval by Clustering Mehrotra, and Ramchandran, 1996), Photobook (Pentland, Picard, and Sclaroff, 1994), Netra (Ma and Manjunath, 1999), RetrievalWare (Dowe, 1993), etc. Actually, the problem of sorting through images to find a particular object of interest is not new. Whether it is paintings in old museum archives, or browsing through the family albums looking for a particular photograph, extracting information from graphic objects has presented many challenges. With the recent advent and growth of the internet, this problem has been taken to a whole new level.
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. Leveson, N. , & Weiss, K. A. (2004). Making embedded software reuse practical and safe. Proceedings of the Twelfth International Symposium on ACM SIGSOFT (pp. 171-178). ACM Press. 16 Genetic Algorithms and Other Approaches in Image Feature Extraction and Representation Limin, W.
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. Los Alamos National Laboratory, Washington DC. Quin, L. ). (1999). XML Specification Guide. John Wiley Press. , & Roy, R.
Artificial intelligence for maximizing content based image retrieval by Zongmin Ma