Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100686
Title: Action search by example using randomized visual vocabularies
Authors: Yuan, Junsong
Liu, Zicheng
Yu, Gang
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2013
Source: Yu, G., Yuan, J., & Liu, Z. (2013). Action Search by Example using Randomized Visual Vocabularies. IEEE Transactions on Image Processing, 22(1), 377-390.
Series/Report no.: IEEE transactions on image processing
Abstract: Because actions can be small video objects, it is a challenging problem to search for similar actions in crowded and dynamic scenes when a single query example is provided. We propose a fast action search method that can efficiently locate similar actions spatiotemporally. Both the query action and the video datasets are characterized by spatio-temporal interest points. Instead of using a unified visual vocabulary to index all interest points in the database, we propose randomized visual vocabularies to enable fast and robust interest point matching. To accelerate action localization, we have developed a coarse-to-fine video subvolume search scheme, which is several orders of magnitude faster than the existing spatio-temporal branch and bound search. Our experiments on cross-dataset action search show promising results when compared with the state of the arts. Additional experiments on a 5-h versatile video dataset validate the efficiency of our method, where an action search can be finished in just 37.6 s on a regular desktop machine.
URI: https://hdl.handle.net/10356/100686
http://hdl.handle.net/10220/17968
ISSN: 1057-7149
DOI: 10.1109/TIP.2012.2216273
Rights: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TIP.2012.2216273].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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