Artificial Neural Networks and Other Methods of Image Classification

In digital image classification the conventional statistical approaches for image classification use only the gray values. Different advanced techniques in image classification like Artificial Neural Networks (ANN), Support Vector Machines (SVM), Fuzzy measures, Genetic Algorithms (GA), Fuzzy support Vector Machines (FSVM) and Genetic Algorithms with Neural Networks are being developed for image classification. Artificial neural networks can handle non-convex decisions. The use of textural features in ANN helps to resolve misclassification. SVM was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. Fuzzy measures show the detection of textures by analyzing the image by stochastic properties. The fundamental stochastic properties of the image are isolated by different kinds of stochastic methods, by non-linear filtering and by non-parametric methods. Fuzzy support vector machines (FSVM) was proposed to overcome the n-class problem in Support Vector Machines. In this using the decision functions obtained by training the SVM, for each class, a truncated polyhedral pyramidal membership function was defined. The genetic algorithm searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. The use of a hybrid genetic algorithm investigates the effectiveness of the genetic algorithm evolved neural network classifier and its application to the image classification of remotely sensed multispectral imagery. A comparative study of some of these techniques for image classification is made to identify relative merits.