  Dynamic texture and scene classification are two fundamental problems in
understanding natural video content. Extracting robust and effective features
is a crucial step towards solving these problems. However the existing
approaches suffer from the sensitivity to either varying illumination, or
viewpoint changing, or even camera motion, and/or the lack of spatial
information. Inspired by the success of deep structures in image
classification, we attempt to leverage a deep structure to extract feature for
dynamic texture and scene classification. To tackle with the challenges in
training a deep structure, we propose to transfer some prior knowledge from
image domain to video domain. To be specific, we propose to apply a
well-trained Convolutional Neural Network (ConvNet) as a mid-level feature
extractor to extract features from each frame, and then form a representation
of a video by concatenating the first and the second order statistics over the
mid-level features. We term this two-level feature extraction scheme as a
Transferred ConvNet Feature (TCoF). Moreover we explore two different
implementations of the TCoF scheme, i.e., the \textit{spatial} TCoF and the
\textit{temporal} TCoF, in which the mean-removed frames and the difference
between two adjacent frames are used as the inputs of the ConvNet,
respectively. We evaluate systematically the proposed spatial TCoF and the
temporal TCoF schemes on three benchmark data sets, including DynTex, YUPENN,
and Maryland, and demonstrate that the proposed approach yields superior
performance.
