978-1-7281-7734-2/20/$31.00 ©2020 IEEE A filter based genetic algorithm and neural network technique for Image Classification Purushottam Das Department of Computer Sc. & Engg. Graphic Era Deemed to be University Dehradun, India purushottamdas82@gmail.com Shambhu Prasad Sah Department of Computer Sc. & Engg. Graphic Era Hill University Bhimtal, India somusuman@gmail.com Dinesh C. Dobhal Department of Computer Sc. & Engg. Graphic Era Deemed to be University Dehradun, India dineshdobhal@gmail.com Dikendra K. Verma Department of ECE Graphic Era Hill University Bhimtal, India dikendraverma@gmail.com Ankur Singh Bist Department of Computer Sc. & Engg. Graphic Era Hill University Bhimtal, India ankur1990bist@gmail.com Saurabh Pargaien Department of ECE Graphic Era Hill University Bhimtal, India saurabhpargaien@gmail.com Abstract—We present a method for classification of images using GA-NN approach. We will take standard image data set from UCI repository or we can create image data set by clicking images using camera and further extracting features using mazda software. In either case we will be getting feature- set of images, which will be reduced (optimized) by feature subset selection using Mutual Information based filter approach of Genetic algorithm. This optimal feature set will be further classified using neural network (nprtool). Finally we are comparing our classification results with the results of existing Multi-SVM method. Keywords—Classification, UCI, feature-set, feature subset selection, genetic algorithm, mutual information, neural network, Multi-SVM. I. INTRODUCTION In last few years researchers have been trying to find a solution that prevents human errors in classification [1, 2, 3]. In the quality control system computer vision system can replace human operator. The operator may lose focus after working for so many hours and that may affect the evolution process. In computer vision system, we are more prompt at accuracy and speed. Here, we have various varieties of grains, that make our classification process much more complicated. Our study displayed that the classification results are good when we are having discriminating features of various varieties. If we have much similarity among classes then classification results will degrade. The objective of work being presented here is to identify the most promising features of the images out of the total features extracted using feature extraction tool ( in this case mazda ). The most discriminating set of features was short listed using the statistical method and then classification performance was taken using neural networks. To classify image into different categories differentiating features of the images should be available. In this project the differentiating features are extracted using the Mazda software. The feature of the input image is then matched with the available features of different categories. If the input features closely match with the available features then the image can be placed into the matched category. Classification process includes many steps such as given below [4]: 1) Image Acquisition 2) Pre - processing 3) Feature Extraction 4) Feature Selection 5) Classification II. LITERATURE SURVEYED Classification is the process of categorizing samples in their classes by using most similar and most discriminating features [5, 6]. Classification used in every field nowadays starting from pattern recognition to artificial intelligence, image classification etc. [7, 8, 9]. Regression is also used for obtaining better results [10]. Features with higher similarity are categorized in same class and features which discriminates each other are classified in different classes. Further Classification used in categorizing image data set is known as image classification. Image classification deals with images. We can take some standard feature data set of images from the UCI repository or we can create our own data set by clicking images using camera of higher resolution. Classification can be of categorized in two types: Supervised classification and unsupervised classification. In supervised classification, we need a trained function while in unsupervised learning no human training is needed [11, 12]. We are selecting only relevant features. Relevant features refer to those features which most discriminates the given data. Once we are having a feature data set. Afterwards we will try to reduce this feature data set using optimization algorithm. Here, we will be using Genetic algorithm for this purpose [13, 14]. Moreover, we will be using Filter approach i.e., Mutual information for optimizing feature set. We will apply classification on the optimized feature set, i.e., feature subset selected after optimizing [15, 16]. We have used most discriminating features for classifying images. First these features are extracted from image data set or we can directly take some standard feature 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI) | 978-1-7281-7734-2/20/$31.00 ©2020 IEEE | DOI: 10.1109/ICATMRI51801.2020.9398498 Authorized licensed use limited to: Raytheon Technologies. Downloaded on April 14,2021 at 09:22:56 UTC from IEEE Xplore. Restrictions apply.