By Zongmin Ma

ISBN-10: 1605661740

ISBN-13: 9781605661742

ISBN-10: 1605661759

ISBN-13: 9781605661759

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 photo Retrieval discusses significant points of content-based picture retrieval (CBIR) utilizing present applied sciences and functions in the synthetic intelligence (AI) box. offering cutting-edge study from prime foreign specialists, this booklet bargains a theoretical viewpoint and sensible suggestions for academicians, researchers, and practitioners.

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The proposed system has a learning phase to understand the images in the database. The learning phase is comprised of different parts. First, a feature vector is calculated for each image. 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.

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.

Object p is directly density-reachable from q if q is a core object and p is in the ε-neighborhood of q. Object p is density-reachable from object q if there is a chain of objects 1n, where 1 = q and n = p such that j+1 is directly density-reachable from j. The core-distance is the smallest distance between a given object p and an object in its ε-neighborhood such that p would be a core object with respect to . The reachability-distance of a given object p is the smallest distance such that p is densityreachable from a core object o.

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Artificial intelligence for maximizing content based image retrieval by Zongmin Ma


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