Multi-scale feature learning for dynamic scene classification
Lu Wang, Shengrong Gong, Yi Ji, Chunping Liu · 2014
In this paper, contrary to most existing hand-crafted descriptor, we propose an automatic feature learning method to solve the problem of dynamic natural scenes classification. Our model use convolutional Restricted Boltzmann machine as building block, called Temporal-Spatial Deep Belief Network (TS-DBN). We train the model over both fine-scale and coarse-scale, which automatically selected from the scale space according to the related information theory knowledge, to learn multi-scale features from each video sequence. The results on Maryland dataset show that feature representation based on automatic deep feature learning methods can achieve comparable accuracy with hand-crafted descriptors. Simultaneously, both coarse and fine-scale features can get better accuracy as compared to single scale features.