Gray Image Recognition Using Hopfield Neural Network With Multi-Bitplane and Multi-Connect Architecture

Abstract

In this work, a method for applying Hopfield Neural Network (HNN) with gray images is presented. Hopfield networks are iterative Auto-Associative networks consisting of a single layer of fully connected processing elements thus categorizes as an associative memory. Associative memories provide one approach to the computer-engineering problem of storing and retrieving data which is based on content rather than storage address. HNN deals with the bipolar system (i.e. -1 and +1) for direct input data, however it is useful for binary images, but unuseful for gray-level or color images unless we suppose another way for input data of such images. To overcome this obstacle, one can suppose for 8-bit gray-level image that consists of 8-layers (bitplanes) of binaries can be represented as bipolar data. In this way it is possible to express each bitplane as single binary image for HNN. The experimental results showed the usefulness of using HNN in gray-level images recognition with good results. Furthermore, there are no limitations to the number of 8-bit gray level images that can be stored in the net memory with the same efficient results.