Jointly Modeling Static Visual Appearance and Temporal Pattern for Unsupervised Video Hashing

Recently, hashing has been evidenced as an efficient and effective method to facilitate large-scale video retrieval. Most of existing hashing methods are based on visual features, which are expected to capture the appearance of videos. The intrinsic temporal pattern embedded in videos has also shown its discriminative power for similarity search, and is explored and utilised in some recent studies. However, how to leverage the strengths in both aspects remains unknown.