An Image Querying In Clustered
Multimedia Databases
Author(s)
J. S. Sandhu & U. S. Pannu
Abstract
Information retrieval from multimedia repositories requires the development of
techniques to supplement traditional methods based on textual descriptions and
searches. The two main reasons that make it necessary to work on new
techniques stated above are: high cost factors in associating textual data with
images and, what is even more important, inadequate characterization by textual
descriptions for subsequent retrieval. In recent years distributed multimedia
databases have become an active area of research. Recent studies confirm that
mankind will generate more image information over the next few years than was
created in the whole of the previous era. Thus, governments, industries and IT
services providers will have to manage a tremendous amount of image data.
Such systems are essential to effectively and efficiently use the existing huge
collection of image data. The major issue while developing such a system is
"efficient search strategy". This paper addresses search strategy by which an
image can be retrieved from a distributed multimedia database in less time and at
less cost. Various academics and developers have been attracted to this topic and
even systems such as QBIC (content based image retrieval system for IBM) have
been released. These systems retrieve images based on their content. A problem
faced in this system is the processing of complex queries such as when retrieving
images having similar texture, color and other features. These difficulties are
overcome by "query by example", i.e. image retrieval by image query. Now,
recent image retrieval systems like COMPASS, using image query strategy, use
new possible descriptors like texture and luminous intensity. In this paper we are
trying to speed up the present search strategy by using only two descriptors. We
are also trying to optimize image retrieval results by using clustering.
Feature-based histograms are studied and, based on their dissimilarities, effective
measures can be taken. This will result in the design of scalable image retrieval
systems, which make optimal use of computational and storage resources.
Keywords: image querying, eccentricity, scale normalization, rotation
normalization, shape index, color index.
Keywords
image querying, eccentricity, scale normalization, rotation
normalization, shape index, color index
Related Book
Other papers in this volume
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