Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
Notions of associative memory and sparse coding
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Increasing the Capacity of a Hopfield Network without Sacrificing Functionality
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A Feature-Based Vehicle Tracking System in Congested Traffic Video Sequences
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Global and local synchrony of coupled neurons in small-world networks
Biological Cybernetics
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Information and Topology in Attractor Neural Networks
Neural Computation
Memory capacities for synaptic and structural plasticity
Neural Computation
Automatic traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems
A generic approach to simultaneous tracking and verification in video
IEEE Transactions on Image Processing
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The goal of this work is to learn and retrieve a sequence of highly correlated patterns using a Hopfield-type of attractor neural network (ANN) with a small-world connectivity distribution. For this model, we propose a weight learning heuristic which combines the pseudo-inverse approach with a row-shifting schema. The influence of the ratio of random connectivity on retrieval quality and learning time has been studied. Our approach has been successfully tested on a complex pattern, as it is the case of traffic video sequences, for different combinations of the involved parameters. Moreover, it has demonstrated to be robust with respect to highly variable frame activity.