Stage classification using two-stream deep convolutional neural networks

Abstract

Stage classification is a significant important task for scene understanding, 3D TV, autonomous vehicle, and object localization. Images can be categorized into a limited number of 3D scene geometries, called stages, and each one of them is having a unique depth pattern to provide a specific context for stage objects. Moreover, convolutional neural networks (CNN) have shown high performance of scene classification due to their powerful perspective of feature learning and reasoning. However, we found that edge-preserving Laplacian filter (LF) based on Laplacian pyramids, which enhances the edge details of image scene owing to this, it can improve the performance of stage classification. We introduce a novel method of stage classification based on two-stream CNN model in which one stream is encoded by LF, and another stream is normal RGB images and their output is fused at the decision level. This proposed method is evaluated on two different stage datasets: first 'stage-1209' contains 1209 images, and second, '12-scene' image dataset contains 12,000 images. Results exhibited that LF encoded images have a positive influence on stage classification accuracy. Following this, while using product rule the proposed method obtains the most significant improvement in the stage classification for both datasets. It improves particularly 7.96% stage accuracy on 12-scene image dataset, compared to the state-of-the-art method.