  Recently, with the enormous growth of online videos, fast video retrieval
research has received increasing attention. As an extension of image hashing
techniques, traditional video hashing methods mainly depend on hand-crafted
features and transform the real-valued features into binary hash codes. As
videos provide far more diverse and complex visual information than images,
extracting features from videos is much more challenging than that from images.
Therefore, high-level semantic features to represent videos are needed rather
than low-level hand-crafted methods. In this paper, a deep convolutional neural
network is proposed to extract high-level semantic features and a binary hash
function is then integrated into this framework to achieve an end-to-end
optimization. Particularly, our approach also combines triplet loss function
which preserves the relative similarity and difference of videos and
classification loss function as the optimization objective. Experiments have
been performed on two public datasets and the results demonstrate the
superiority of our proposed method compared with other state-of-the-art video
retrieval methods.
