Hybrid of Statistical and Spectral Texture Features for Vehicle Object Classification System

Objectives: To increase the performance of the classifier for the vehicle object among a mixed and highly texture background using hybrid feature extraction method without pre-processing. Methods/Analysis: Vehicle Object recognition system performance is based on the hybrid of feature vector extraction method and artificial neural network classifier without pre-processing. Every image is divided into single blocks size with 20x20 each. The feature vector is extracted from each single size block of the picture. Normalization is done for the extracted feature vector of the vehicle object in the image. These feature vectors are given as input to the neural network classifier for classification. The feed forward Back Propagation Neural Network (BPNN) algorithm is used to train and test the input feature vector by using Neural Network Classifier (NNC) for the vehicle classification. Findings: The idea of the proposed method is that combining the two different literatures namely statistical and spectral texture features without pre-processing for classification. This method is trained and tested with Illinois at Urbana-Champaign (UIUC) standard databases. UIUC database contains car and non-car images with mixed and highly textured background. The findings indicate that the selected input feature vector improves the classification accuracy rate compared to the previous literature. Also the hybrid features maximize the correct classification and minimize the wrong classification. The improved performance results 90.1% of quantitative evaluation is compared with different literature methods of similar work. Applications/Improvements: In different applications, the proposed method plays vital part in surveillance, security for vehicles, monitoring the traffic, etc.