Please use this identifier to cite or link to this item:
|Title:||Marginalized multi-layer multi-instance kernel for video concept detection|
|Authors:||Zha, Z.-J. |
Video concept detection
|Source:||Zha, Z.-J., Mei, T., Hong, R., Gu, Z. (2013-08). Marginalized multi-layer multi-instance kernel for video concept detection. Signal Processing 93 (8) : 2119-2125. ScholarBank@NUS Repository. https://doi.org/10.1016/j.sigpro.2012.08.026|
|Abstract:||Video concept detection has been extensively studied in recent years. Most of the existing video concept detection approaches have treated video as a flat data sequence. However, video is essentially a kind of media with hierarchical structure, including multiple layers (e.g., video shot, frame, and region) and multiple instance relationship embedded in each pair of contiguous layers. In this paper, we propose a novel kernel, termed marginalized multi-layer multi-instance (MarMLMI) kernel for video concept detection. Different from most existing methods, the proposed MarMLMI kernel exploits the hierarchical structure of video, i.e., both the multi-layer structure and the multi-instance relationship. Furthermore, the instance label ambiguity in multi-instance setting is addressed by using the technology of marginalized kernel. We perform video concept detection on a real-world video corpus: the TREC video retrieval evaluation (TRECVID) benchmark and compare the proposed MarMLMI kernel to representative existing approaches. The experimental results demonstrate the effectiveness of the proposed MarMLMI kernel. © 2012 Elsevier B.V.|
|Source Title:||Signal Processing|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 22, 2018
WEB OF SCIENCETM
checked on Jan 24, 2018
checked on Feb 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.