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Article
Association-based image retrieval
Computer Science Faculty Publications and Presentations
  • Arun D. Kulkarni, University of Texas at Tyler
  • H. Gunturu
  • S. Dalta
Abstract
With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed. In order to extract useful information from this huge amount of data, many content-based image retrieval (CBIR) systems have been developed in the last decade. A typical CBIR system captures image features that represent image properties such as color, texture, or shape of objects in the query image and try to retrieve images from the database with similar features. Recent advances in CBIR systems include relevance feedback based interactive systems. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, we propose a new approach for image storage and retrieval called association-based image retrieval (ABIR). We try to mimic human memory. The human brain stores and retrieves images by association. We use a generalized bi-directional associative memory (GBAM) to store associations between feature vectors. The results of our simulation are presented in the paper.
Publisher
WSEAS Transactions on Signal Processing
Date of publication
7-1-2007
Language
English
Persistent identifier
http://hdl.handle.net/10950/336
Document Type
Article
Subject Categories
Publisher Citation
Kulkarni A. D., Gunturu, H., and Datla S. (2008). Association-based image retrieval. WSEAS Transactions on Signal Processing, vol. 4, no. 4, pp 183-189.
Citation Information
Arun D. Kulkarni, H. Gunturu and S. Dalta. "Association-based image retrieval" (2007)
Available at: http://works.bepress.com/arun-kulkarni/3/