A Nfl-based and Feature Extraction Supported Shot Retrieval Approach

DOI

10.2991/iccasm.2012.272How to use a DOI?

Keywords

Nearest Feature Line, Feature Extraction, shot retrieval, key frame extraction

Abstract

Nearest Feature Line (NFL) is a convenient and effective way to search in video database, but the Framework does not address the feature extraction for dimension reduction. In this paper, a novel method is proposed for content-based shot retrieval. Karhunen Loeve Transform (KLT) is used to reduce dimensionality of feature spaces. In addition, we present a new take-maximum-from-minima (TMFM) based key frame extraction algorithm, and keyframes extraction is combined with the NFL method to achieve a better performance. Experimental results have shown that our combined method not only achieves superior performance than the NFL and Equal interval (EI), and other classification methods such as Nearest Neighbor (NN) and EI and Nearest Center (NC) and EI but also increases the retrieval speed and reduces the memory significantly without sacrificing the retrieval accuracy.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhen Lei
AU  - Yujun Liu
AU  - Wenge Zhang
AU  - Xuelin Liu
PY  - 2012/08
DA  - 2012/08
TI  - A Nfl-based and Feature Extraction Supported Shot Retrieval Approach
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 1072
EP  - 1075
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.272
DO  - 10.2991/iccasm.2012.272
ID  - Lei2012/08
ER  -