Detecting frequent patterns in video using partly locality sensitive hashing
K Ogawara, Y Tanabe, R Kurazume… - Computer Vision–ACCV …, 2011 - Springer
Computer Vision–ACCV 2010 Workshops: ACCV 2010 International Workshops …, 2011•Springer
Frequent patterns in video are useful clues to learn previously unknown events in an
unsupervised way. This paper presents a novel method for detecting relatively long variable-
length frequent patterns in video efficiently. The major contribution of the paper is that Partly
Locality Sensitive Hashing (PLSH) is proposed as a sparse sampling method to detect
frequent patterns faster than the conventional method with LSH. The proposed method was
evaluated by detecting frequent everyday whole body motions in video.
unsupervised way. This paper presents a novel method for detecting relatively long variable-
length frequent patterns in video efficiently. The major contribution of the paper is that Partly
Locality Sensitive Hashing (PLSH) is proposed as a sparse sampling method to detect
frequent patterns faster than the conventional method with LSH. The proposed method was
evaluated by detecting frequent everyday whole body motions in video.
Abstract
Frequent patterns in video are useful clues to learn previously unknown events in an unsupervised way. This paper presents a novel method for detecting relatively long variable-length frequent patterns in video efficiently. The major contribution of the paper is that Partly Locality Sensitive Hashing (PLSH) is proposed as a sparse sampling method to detect frequent patterns faster than the conventional method with LSH. The proposed method was evaluated by detecting frequent everyday whole body motions in video.
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