Many existing techniques in content based video retrieval

treat a video sequence as a whole to match it against a query

video or to assign a text label. Such an approach has serious

limitations when applied to human action retrieval because

an action may occur only in a sub-region and last for a small

portion of the video length. In situations like this, we essen-

tially need to match the subvolumes of the video sequences

against the query video. A naive exhaustive search is im-

practical due to large number of possible subvolumes for each

video sequence. In this paper, we propose a novel framework

for action retrieval which performs pattern matching at sub-

volume level and is very efficient in handling large corpus of

videos. We construct an unsupervised random forest to in-

dex the video database, generate a score volume with Hough

voting and then employ a max sub-path strategy to quickly

search for the temporal and spatial positions of all the video

sequences in the database. We present action search experi-

ments on challenging datasets to validate the efficiency and

effectiveness of our system.

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