Multi-context Deep Convolutional Features and Exemplar-SVMs for Scene Parsing

Scene parsing is a challenging task in computer vision field. The work of scene parsing is labeling every pixel in an image with its semantic category to which it belongs. In this paper, we solve this problem by proposing an approach that combines the multi-context deep convolutional features with exemplar-SVMs for scene parsing. A convolutional neural network is employed to learn the multi-context deep features which include image global features and local features. In contrast to hand-crafted feature extraction approaches, the convolutional neural network learns features automatically and the features can better describe images on the task. In order to obtain a high class recognition accuracy, our system consists of the exemplar-SVMs which is training a linear SVM classifier for every exemplar in the training set for classification. Finally, multiple cues are integrated into a Markov Random Field framework to infer the parsing result. We apply our system to two challenging datasets, SIFT Flow dataset and the dataset which is collected by ourselves. The experimental results demonstrate that our method can achieve good performance.