Object Classification in Static Images with Cluttered Background using Statistical Feature Based Neural Classifier

摘要

Object classification in static images is a difficult task since motion information in no longer usable. The challenging task in object classification problem is the removal of cluttered background containing trees, road views, buildings and occlusions. The goal of this study is to build a system that detects and classifies the car objects amidst background clutter and mild occlusion. This study addresses the issues to classify objects of real-world images containing side views of cars with cluttered background with that of non-car images with natural scenes. The threshold technique with background subtraction is used to segment the background region to extract the object of interest. The background segmented image with region of interest is divided into equal sized blocks of sub-images. The statistical features are extracted from each sub-block. The features of the objects are fed to the back-propagation neural classifier. Thus, the performance of the neural classifier is compared with various categories of block size. Quantitative evaluation shows improved results of 83.8%. A critical evaluation of our approach under the proposed standards is presented.