Enhanced Walsh Function Based Distributed Associative Memory for Pattern Recognition

Abstract

A Walsh function based distributed associative memory is capable of storing multiple patterns in a single storage space with Walsh encoding of each pattern. This Walsh-based associative memory has unique advantages in aspects of both reduced storage and fast recall compared to other types of associative memories. However, the price to pay for these incredible benefits is the amount of crosstalk among stored patterns that sometimes leads to mis-recognition of some of the stored items. In this paper, this adverse effect arising from the superimposed storage can be greatly alleviated by optimizing the different sequencies of the Walsh functions associated with each pattern to be stored. This optimization also lends itself to maximize the memory capacity by jamming more patterns onto the same memory space still maintaining perfect recognition. In order to verify its efficiency, we successfully applied the Walsh-based memory to high speed face recognition with much reduced data storage.