This paper presents a new deep learning approach for video-based scene
classification. We design a Heterogeneous Deep Discriminative Model (HDDM)
whose parameters are initialized by performing an unsupervised pre-training in
a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBM). In
order to avoid the redundancy of adjacent frames, we extract spatiotemporal
variation patterns within frames and represent them sparsely using Sparse Cubic
Symmetrical Pattern (SCSP). Then, a pre-initialized HDDM is separately trained
using the videos of each class to learn class-specific models. According to the
minimum reconstruction error from the learnt class-specific models, a weighted
voting strategy is employed for the classification. The performance of the
proposed method is extensively evaluated on two action recognition datasets;
UCF101 and Hollywood II, and three dynamic texture and dynamic scene datasets;
DynTex, YUPENN, and Maryland. The experimental results and comparisons against
state-of-the-art methods demonstrate that the proposed method consistently
achieves superior performance on all datasets.

本文提出了一种新的深度学习方法来处理基于视频的场景分类问题，使用 HDDM 模型，通过异构无监督预训练和 SCSP 来解决存储问题，并使用加权投票策略进行分类，实验结果表明该方法在所有数据集上都实现了卓越的性能。