Research And Application Of Image Classification Based On Postive And Negative Fuzzy System

Digital image processing technology develops rapidly with the development of computer science and technology and the increasing popularity of computer networks. As an important branch of image processing, the image classification provides a lot of important information for further experiments and researches. The technology of image classification is a specific application of pattern recognition in the field of digital image processing; its main purpose is to develop a computer intelligent system which could replace human beings to complete the task of image classification and recognition. In recent years, people made many efforts in this regard and have achieved remarkable results. At present, the main method of image classification are support vector machine (SVM), knowledge-based image classification, fuzzy set theoryand artificial neural network method, etc. Support vector machine is widely used in image classification, but its performance is greatly affected by the parameters, and parameter selection method are not yet comprehensive or complete; Knowledge-based image classification methods based on expert knowledge and experience attract wide attention from many scientific researchers, while most of the knowledge and experience belong to a particular geographical and time domain, and self-learning is the problem need to be resolved; Fuzzy set method which has good flexibility and adaptability can well solve the ambiguity problem, but the choice of membership function mainly relies on experiences with great subjectivity and blindness; Artificial neural network method using computer to simulate self-learning process of human beings and of strong non-linear approximation capability, has been widely used in information processing and pattern recognition.Most of the current image classification algorithms only use the positive rules in fuzzy system, while negative rules also provide a lot of useful information. This paper focuses on the positive and negative fuzzy rule system and its application to image classification. Based on positive and negative fuzzy rule system which effective combinates of positive and negative fuzzy rules, we propose three positive and negative fuzzy system with anti-noise performance and better classification performance. This work will focus on:1, A kind of positive and negative fuzzy system for image classification is improved, which has obviously advantages compared to positive fuzzy rule systerm or negative fuzzy rule systerm. We detailly describe the structure, the type of membership functions and parameters’adjustment methods of positive and negative fuzzy system. A series of experiments show that this method with better noise immunity can classify not only remote sensing image, but also natural images.2, On this basis, since their method heavily suffers from the very slow learning speed for the training and easily falling in local minima of the cost function of the network which is realized using the feedforward neural network model which requires adjusting the weights in the gradient descent way, Extreme Learning Machine (ELM) theory is introduced. It is the effective way that can avoid the above problems and the input weights and hidden layer bias can be randomly assignment when the hidden layer activation function infinitely differentiable. In this paper, the positive and negative fuzzy system based ELM is proposed for image classification and a series of experiments are done on remote sensing images and natural images. Image classification results show that the method has better classification effect and anti-noise effect, and also has fast learning speed, generalization performance.3, The Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). In order to reduce the number of nodes in the hidden layer and increase the flexibility of neural networks, we propose a hidden feature space ridge regression (HFSR) and expanse to a multi-hidden layer feedforward neural network (MLFN). The parameters in the last layer of hidden layers of the MLFN’s HFSR can be obtaned by Moore-Penrose generalized inverse method, and other parameters in hidden layer can be arbitrarily assigned while it is a simple and fast algorithm just like ELM which only needs to train one time. A positive and negative fuzzy rule system using ridge regression for extremely fast Image classification is proposed in the paper. A series of experiments show that the proposed method has better image classification results, and also has very good noise immunity.