By Zongmin Ma
The expanding pattern of multimedia info 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 photo info.
Artificial Intelligence for Maximizing content material dependent snapshot Retrieval discusses significant elements of content-based photograph retrieval (CBIR) utilizing present applied sciences and purposes in the synthetic intelligence (AI) box. delivering cutting-edge examine from best overseas specialists, this publication deals a theoretical point of view and functional ideas for academicians, researchers, and practitioners.
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Extra info for Artificial intelligence for maximizing content based image retrieval
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.
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.
These vectors are then clustered so that similar images are grouped together; we use OPTICS clustering approach which does not require the number of clusters be specified in advance. In the querying phase, the query image is first classified, and a feature vector is calculated for the image. The vector is compared to representatives of the clusters only, instead of every stored image in the database. After locating the cluster representing the closest match, the query image is compared to the images within that cluster and the best matches are returned to the user.