LLE-based video hashing for video identification

As web video databases tend to contain immense copies with the explosive growth of online videos, effective and efficient copy identification techniques are required for content management and copyrights protection. To this end, this paper presents a novel video hashing for video copy identification based on Locally Linear Embedding (LLE). It maps the video to a low-dimensional space through LLE, which is invariant to translation, rotation and rescaling. In this way, we can use the points mapped from the video to play as a robust hashing. Meanwhile, to detect copies which are parts of original videos or contain a clip that comes from original. A dynamic sliding window is applied for matching. Experimental results show that the video hashing is of good robustness and discrimination.