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Content-Based Image Retrieval Using Associative Memories
6th WSEAS International Conference on Telecommunications and Informatics (2007)
  • Dr. Arun D Kulkarni, University of Texas at Tyler
The rapid growth in the number of large-scale repositories has brought the need for
efficient and effective content-based image retrieval (CBIR) systems. The state of the art in the
CBIR systems is to search images in database that are “close” to the query image using some
similarity measure. The current CBIR systems capture image features that represent properties
such as color, texture, and/or shape of the objects in the query image and try to retrieve images
from the database with similar features. In this paper, we propose a new architecture for a CBIR
system. We try to mimic the human memory. We use generalized bi-directional associative
memory (BAMg) to store and retrieve images from the database. We store and retrieve images
based on association. We present three topologies of the generalized bi-directional associative
memory that are similar to the local area network topologies: the bus, ring, and tree. We have
developed software to implement the CBIR system. As an illustration, we have considered three
sets of images. The results of our simulation are presented in the paper.
  • Content-based Image Retrieval,
  • Bi-directional Associative Memories,
  • Multi Media Databases
Publication Date
March, 2007
Dallas, TX, U.S.A.
Citation Information
Arun D Kulkarni. "Content-Based Image Retrieval Using Associative Memories" 6th WSEAS International Conference on Telecommunications and Informatics (2007)
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