Efficient Video Retrieval by Locality Sensitive Hashing
Shiyan Hu · 2006
In this paper, a new scheme for fast video retrieval is proposed. In the scheme, a video is represented by a set of feature vectors which are computed using the robust alpha-trimmed average color histogram. To efficiently retrieve videos, the locality sensitive hashing technique, which involves a uniform distance shrinking projection, is applied. Such a technique does not suffer from the notorious "curse of dimensionality" problem in handling high-dimensional data point sets and guarantees that geometrically close vectors are hashed to the same bucket with high probability. In addition, unlike the conventional techniques, the involved similarity measure incorporates the temporal order of video sequences. The experimental results demonstrate that the proposed scheme outperforms the conventional approaches in accuracy and efficiency.