Cellular automata in pattern recognition

Emerging Applications of Cellular Automata, 2013

Cellular Automata, 2002

This paper reports the design of a Cellular Automata Machine (CAM) to ad-dress the problem of Pattern Recognition. The design is based on an elegant computing model of a particular class of Cellular Automata (CA) referred to as Generalized Multiple Attractor CA (GMACA). The ...

… OF YORK DEPARTMENT OF COMPUTER SCIENCE- …, 1999

A common factor of many of the problems in shape recognition and, in extension, in image interpretation is the large dimensionality of the search space. One way to overcome this situation is to partition the problem into smaller ones and combine the local solutions towards global interpretations. Using this approach, the system presented in this thesis provides a novel combination of the descriptional power of symbolic representations of image data, the parallel and distributed processing model of cellular automata and the speed and robustness of connectionist symbolic processing.

Complexity, 2016

This article designs an efficient two‐class pattern classifier utilizing asynchronous cellular automata (ACAs). The two‐state three‐neighborhood one‐dimensional ACAs that converge to fixed points from arbitrary seeds are used here for pattern classification. To design the classifier, (1) we first identify a set of ACAs that always converge to fixed points from any seeds, (2) each ACA should have at least two but not huge number of fixed point attractors, and (3) the convergence time of these ACAs are not to be exponential. To address the second issue, we propose a graph, coined as fixed point graph of an ACA that facilitates in counting the fixed points. We further perform an experimental study to estimate the convergence time of ACAs, and find there are some convergent ACAs which demand exponential convergence time. Finally, we identify there are 73 (out of 256) ACAs which can be effective candidates as pattern classifier. We use each of the candidate ACAs on some standard datasets...

Fundamenta Informaticae, 2003

This paper presents the theory and application of a high speed, low cost pattern classifier. The proposed classifier is built around a special class of sparse network referred to as Cellular Automata (CA). A specific class of CA, termed as Multiple Attractor Cellular Automata (MACA), has been evolved through Genetic Algorithm (GA) formulation to perform the task of pattern classification. The versatility of the classification scheme is illustrated through its application in three diverse fields -data mining, image compression, and fault diagnosis. Extensive experimental results demonstrate better performance of the proposed scheme over popular classification algorithms in respect of memory overhead and retrieval time with comparable classification accuracy. Hardware architecture of the proposed classifier has been also reported.

Lecture Notes in Computer Science, 2005

As the possibility of combining different classifiers into Multiple Classifier System (MCS) becomes an important direction in machine-learning, difficulties arise in choosing the appropriate classifiers to combine and choosing the way for combining their decisions. Therefore in this paper we present a novel approach -Classificational Cellular Automata (CCA). The basic idea of CCA is to combine different classifiers induced on the basis of various machine-learning methods into MCS in a non-predefined way. After several iterations of applying adequate transaction rules only a set of the most appropriate classifiers for solving a specific problem is preserved.

Cellular learning automata (CLA) is a distributed computational model which was introduced in the last decade. This model combines the computational power of the cellular automata with the learning power of the learning automata. Cellular learning automata is composed from a lattice of cells working together to accomplish their computational task; in which each cell is equipped with some learning automata. Wide range of applications utilizes CLA such as image processing, wireless networks, evolutionary computation and cellular networks. However, the only input to this model is a reinforcement signal and so it cannot receive another input such as the state of the environment. In this paper, we introduce a new model of CLA such that each cell receives extra information from the environment in addition to the reinforcement signal. The ability of getting an extra input from the environment increases the computational power and flexibility of the model. We have designed some new algorithms for solving famous problems in pattern recognition and machine learning such as classification, clustering and image segmentation. All of them are based on the proposed CLA. We investigated performance of these algorithms through several computer simulations. Results of the new clustering algorithm shows acceptable performance on various data sets. CLA-based classification algorithm gets average precision 84% on eight data sets in comparison with SVM, KNN and Naive Bayes with average precision 88%, 84% and 75%, respectively. Similar results are obtained for semi-supervised classification based on the proposed CLA.

Series in Computer Vision, 2011

An overview is given on the use of cellular automata for image processing. We first consider the number of patterns that can exist in a neighbourhood, allowing for invariance to certain transformation. These patterns correspond to possible rules, and several schemes are described for automatically learning an appropriate rule set from training data. Two alternative schemes are given for coping with gray level (rather than binary) images without incurring a huge explosion in the number of possible rules. Finally, examples are provided of training various types of cellular automata with various rule identification schemes to perform several image processing tasks.

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2004

This paper reports a cellular automata (CA) based model of associative memory. The model has been evolved around a special class of CA referred to as generalized multiple attractor cellular automata (GMACA). The GMACA based associative memory is designed to address the problem of pattern recognition. Its storage capacity is found to be better than that of Hopfield network. The GMACA are configured with nonlinear CA rules that are evolved through genetic algorithm (GA). Successive generations of GA select the rules at the edge of chaos [1], [2]. The study confirms the potential of GMACA to perform complex computations like pattern recognition at the edge of chaos.

2005 9th International Workshop on Cellular Neural Networks and Their Applications, 2005

Cellular Systems are defined by cells that have an internal state and local interactions between cells that govern the dynamics of the system. We propose to use a special kind of Cellular Neural Networks (CNNs) which operates in finite iteration discrete-time mode and mimics the processing of visual perception in biological systems for digit recognition. We propose also a solution to another type of pattern recognition problem using a non-standard cellular neural networks called Molecular Graph Networks (MGNs) which offer direct mapping from compound to property of interest such as Physico-Chemical, Toxicity, logP, Inhibitory Activity MGNs translate molecular topology to network topology. We show how to design/train by backpropagation CNNs and MGNs in their discrete-time and finite-iteration versions to perform classification on real-world data sets.