Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Fuzzy-Neural Systems
Computer Vision and Fuzzy-Neural Systems
Content-Based Image Retrieval Based on a Fuzzy Approach
IEEE Transactions on Knowledge and Data Engineering
A new approach to image retrieval with hierarchical color clustering
IEEE Transactions on Circuits and Systems for Video Technology
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Cubic-splines neural network- based system for image retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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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.