Indian Journal of Science and Technology

Abstract

Objectives: To increase the classification accuracy and the performance of the artificial neural network classifier based on hybrid feature extraction. Two different literatures are hybrid together to obtain the better result. Methods/Analysis: Identification and classification of vehicle object system are based on a hybrid feature extraction method and neural classifier. Every image is divided in two equal size 10x10 sub-block. From each sub-block of the image, central moment and geometrical moment features are extracted without pre-processing. The extracted feature vector is normalized and combined together. Normalization is done by using ZScore normalization technique. Then the normalized feature vectors are fed to the Artificial Neural Network (ANN) classifier by using Feed Forward Back Propagation Algorithm (FFBPA) for classifying the vehicle object. Findings: Illinois at Urbana-Champaign (UICI) standard database is used for vehicle object classification. UIUC Dataset contains 500 car images and 500 non-car images with mixed background. The normalized input feature vectors which have been selected are improving the classification accuracy compared with the previous work. It increases the true categorization ratio and decreases the false categorization ratio. The quantity improved performance result shows 95.3% compared with a similar work of various literature methods. Applications/Improvements: This novel method plays a vital role in applications such as vehicle security system, traffic monitoring system etc. 
Keywords: Artificial Neural Network, Back Propagation Algorithm, Central Moment Features, Feature Extraction, Geometrical Moment Features, Hybrid Feature, Normalization, Vehicle Classification