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An Image Recognition/Retrieval System: Design And Implementation

1811 words - 8 pages

Introduction This paper discusses the design and implementation of an image recognition /retrieval system indexed with parameterized color histogram. As the usage of multimedia data has increased in recent years, effective and efficient methods for storing and retrieving multimedia have been required and are being developed. In particular, images are used as important inputs in a variety of areas. The area of content-based image retrieval is a hybrid research area that requires knowledge of both computer vision and of database systems. Large image databases are collected, and images from these collections made available to users in advertising, marketing, entertainment, and other areas where images can be used to enhance the product. The generally categorized images as animals, natural scenes, people, and so on for access require browsing by a user to select the appropriate images. Image Recognition/Retrieval System Over the previous years, techniques for content-based image retrieval systems are published. The focus of this paper is to design and implement the efficient way of retrieving the images by sample query images. A set of color features has been efficiently used to measure the similarity of given images. However, at the same time the size of the color features is too large to implement an indexing scheme effectively. In other words, the large size of color features need to have large amount of resources and the computer systems require much of the time to operate. It has been, therefore, proposed to retrieve similar images by using interpolated color histogram to save resources and to increase efficiency. In order to represent the distribution of the color histograms, the best order of interpolated polynomial would be simulated. After a histogram distribution is represented in a polynomial form, a few numbers of polynomial coefficients are indexed and stored in the database as a color descriptor. As a result, this new approach would have same query results with increased speed saving important resources. Linda G. Shapiro worked on the development of a general, scalable architecture to support fast querying of very large image databases with user-specified distance measures. Algorithms and data structures have been developed for efficient image retrieval from large databases with multiple distance measures. Linda’s research led to discovery of methods for merging general, distance-measure-independent method with other useful techniques that is distance measure specific, such as keyword retrieval and relational indexing. A prototype system, FIDS, with which the user can conduct searches using complex combinations of several dozen distance measures. The new system incorporated a new set of data structures and algorithms into the latest version of FIDS that has led to a marked speed up of the elimination phase of the search in large image databases. Currently, FIDS contains over 37,000 images. A complex search through the database can be...

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