Volume 8 Number 7 (Jul. 2013)
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JSW 2013 Vol.8(7): 1751-1758 ISSN: 1796-217X
doi: 10.4304/jsw.8.7.1751-1758

An Improved Keyframe Extraction Method Based on HSV Colour Space

Zhong Qu, Lidan Lin, Tengfei Gao, Yongkun Wang
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China

Abstract—Video segmentation and keyframe extraction are the basis of Content-based Video Retrieval (CBVR), in which keyframe selection is at the very core of CBVR. At shot level, key-frame extraction provides sufficient indexing and browsing of large video databases. In this paper, we proposed two improved approaches of key-frame extraction for video summarization. In our first synthesis method based on histogram-based method and pixel-based method, videos were firstly segmented into shots according to video content, by our improved histogram-based method, with the use of histogram intersection and nonuniform partitioning and weighting. Then, the obtained results are secondly detected to optimize the results. On the other hand, we realized an improved clustering algorithm for video shot segmentation, in consideration of video characteristics. Within each shot, key-frames were determined with the calculation of image entropy of every frame in HSV colour space. Our simulation results in section 4 prove that extracted key frames with our method are compact and faithful to the original video. Moreover, according to the types of test videos, different methods for shot segmentation are highly recommended.

Index Terms—Content-based Video Retrieval, Video Summary, Video Shot Detection, Keyframe Extraction, Histogram-based Method, Clustering-based Method, Image Entropy.


Cite: Zhong Qu, Lidan Lin, Tengfei Gao, Yongkun Wang, "An Improved Keyframe Extraction Method Based on HSV Colour Space," Journal of Software vol. 8, no. 7, pp. 1751-1758, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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