  Recognizing dynamic scenes is one of the fundamental problems in scene
understanding, which categorizes moving scenes such as a forest fire,
landslide, or avalanche. While existing methods focus on reliable capturing of
static and dynamic information, few works have explored frame selection from a
dynamic scene sequence. In this paper, we propose dynamic scene recognition
using a deep dual descriptor based on `key frames' and `key segments.' Key
frames that reflect the feature distribution of the sequence with a small
number are used for capturing salient static appearances. Key segments, which
are captured from the area around each key frame, provide an additional
discriminative power by dynamic patterns within short time intervals. To this
end, two types of transferred convolutional neural network features are used in
our approach. A fully connected layer is used to select the key frames and key
segments, while the convolutional layer is used to describe them. We conducted
experiments using public datasets as well as a new dataset comprised of 23
dynamic scene classes with 10 videos per class. The evaluation results
demonstrated the state-of-the-art performance of the proposed method.
