Recalling of Images using Hopfield Neural Network Model

05/02/2011
by   C. Ramya, et al.
0

In the present paper, an effort has been made for storing and recalling images with Hopfield Neural Network Model of auto-associative memory. Images are stored by calculating a corresponding weight matrix. Thereafter, starting from an arbitrary configuration, the memory will settle on exactly that stored image, which is nearest to the starting configuration in terms of Hamming distance. Thus given an incomplete or corrupted version of a stored image, the network is able to recall the corresponding original image. The storing of the objects has been performed according to the Hopfield algorithm explained below. Once the net has completely learnt this set of input patterns, a set of testing patterns containing degraded images will be given to the net. Then the Hopfield net will tend to recall the closest matching pattern for the given degraded image. The simulated results show that Hopfield model is the best for storing and recalling images.

READ FULL TEXT
research
10/08/2022

An associative memory model with very high memory rate: Image storage by sequential addition learning

In this paper, we present a neural network system related to about memor...
research
02/01/2022

Content addressable memory without catastrophic forgetting by heteroassociation with a fixed scaffold

Content-addressable memory (CAM) networks, so-called because stored item...
research
10/07/2019

Meta-Learning Deep Energy-Based Memory Models

We study the problem of learning associative memory – a system which is ...
research
12/23/2020

Convolutional Neural Network for Elderly Wandering Prediction in Indoor Scenarios

This work proposes a way to detect the wandering activity of Alzheimer's...
research
04/25/2021

Neurodynamical Role of STDP in Storage and Retrieval of Associative Information

Spike-timing-dependent plasticity (STDP) is a biological process in whic...
research
05/30/2019

A Hippocampus Model for Online One-Shot Storage of Pattern Sequences

We present a computational model based on the CRISP theory (Content Repr...
research
03/19/2003

A Neural Network Assembly Memory Model with Maximum-Likelihood Recall and Recognition Properties

It has been shown that a neural network model recently proposed to descr...

Please sign up or login with your details

Forgot password? Click here to reset