Invariant Object Recognition Using Circular Pairwise Convolutional Networks

Invariant object recognition has been one of the most rewarding are of research in computer vision as there are many applications need the capability of recognizing objects of interest in various environments. However, there is no single technique which claims to achieve the goal in all possible conditions and domains. Out of many techniques, convolutional network has proved to be a good candidate in this area. Given large numbers of training samples of objects under various variation aspects such as lighting, pose, background, etc., convolutional network can learn to extract invariant features by itself. This comes with the price of lengthy training time. Hence, we propose a circular pairwise classification technique to shorten the training time. We compared the recognition accuracy and training time complexity between our approach and a benchmark generic object recognizer LeNet7 which is a monolithic convolutional network.