CLASSIFICATION OF SEVERELY OCCLUDED IMAGE SEQUENCES VIA CONVOLUTIONAL RECURRENT NEURAL NETWORKS

Abstract

Classifying severely occluded images is a challenging yet highly-needed task. In this paper, motivated by the fact that human being can exploit context information to assist learning, we apply convolutional recurrent neural network (CRNN) to attack this challenging problem. A CRNN architecture that integrates convolutional neural network (CNN) with long short-term memory (LSTM) is presented. Three new datasets with severely occluded images and context information are created. Extensive experiments are conducted to compare the performance of CRNN against conventional methods and human experimenters. The experiment results show that the CRNN outperforms both conventional methods and most of the human experimenters. This demonstrates that CRNN can effectively learn and exploit the unspecified context information among image sequences, and thus can be an effective approach to resolve the challenging problem of classifying severely occluded images.