A Deep Learning Framework Using Convolutional Neural Network for Multi-Class Object Recognition

Object recognition is classic technique used to effectively recognize an object in the image. Technologies specifically in field of computer vision are expected to detect and recognize more complex tasks with help of local features detection methods. Over the last decade, there has been sustained increase in the number of researchers from various kind of disciplines i.e. academia, industry, security agencies and even from general public has caught an attention to explore the covered aspects of object detection and recognition concerned problems. It is further significantly amended by adopting deep learning model. In this paper, we applied deep learning to multi-class object recognition and explore convolutional neural network (CNN). The convolutional neural network is created with normalized standard initialization and trained with training set of sample images from 9 different object categories plus sample test images using widely varied dataset. All results are implemented in python tensorflow framework. We examine and compared CNN results with final feature vectors extracted from variant approaches of BOW based on linear L2-SVM classifier. Based on it, sufficient experiments verify our CNN model effectiveness and robustness with rate of 90.12% accuracy.