A key frame extraction method based on mutual information and image entropy

2011 ◽  
Author(s):  
Lina Sun ◽  
Yihua Zhou
Keyword(s):  
Mutual Information ◽  
Extraction Method ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Image Entropy

scholarly journals Within and Between Shot Information Utilisation in Video Key Frame Extraction

2011 ◽  
Vol 10 (03) ◽  
pp. 247-259 ◽  
Author(s):  
Dianting Liu ◽  
Mei-Ling Shyu ◽  
Chao Chen ◽  
Shu-Ching Chen
Keyword(s):  
Extraction Method ◽  
Video Sequence ◽  
Video Retrieval ◽  
Video Browsing ◽  
High Quality ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Video Shot ◽  
Key Frames ◽  
Candidate Set

In consequence of the popularity of family video recorders and the surge of Web 2.0, increasing amounts of videos have made the management and integration of the information in videos an urgent and important issue in video retrieval. Key frames, as a high-quality summary of videos, play an important role in the areas of video browsing, searching, categorisation, and indexing. An effective set of key frames should include major objects and events of the video sequence, and should contain minimum content redundancies. In this paper, an innovative key frame extraction method is proposed to select representative key frames for a video. By analysing the differences between frames and utilising the clustering technique, a set of key frame candidates (KFCs) is first selected at the shot level, and then the information within a video shot and between video shots is used to filter the candidate set to generate the final set of key frames. Experimental results on the TRECVID 2007 video dataset have demonstrated the effectiveness of our proposed key frame extraction method in terms of the percentage of the extracted key frames and the retrieval precision.

Key-Frame Extraction Method from Single-Lens Undersea Video Sequences

2013 ◽  
Vol 404 ◽  
pp. 514-519
Author(s):  
Xiu Li ◽  
Fu Xin Gao ◽  
Tian Xiang Yan ◽  
Dong Zhi Wang ◽  
Lian Sheng Chen ◽  
...  
Keyword(s):  
Extraction Method ◽  
Video Sequences ◽  
Underwater Video ◽  
Key Frame Extraction ◽  
Brightness Change ◽  
Key Frame ◽  
Noise Point ◽  
Single Lens ◽  
Shot Boundary

The process of key-frame extraction of the undersea video is different from that on the land. The effective key-frame extraction will promote research and retrieval of underwater video. In this paper, we first introduced the characteristics of the undersea video, and then proposed a new key-frame extraction method based on Sensitive Curve brightness change for single-lens undersea video sequences which measures the light shot boundary brightness change. The experiment results show that the proposed algorithm can extract key information of the undersea video quickly, and have a good performance for the noise point.

scholarly journals A Fast and Robust Key Frame Extraction Method for Video Copyright Protection

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yunyu Shi ◽  
Haisheng Yang ◽  
Ming Gong ◽  
Xiang Liu ◽  
Yongxiang Xia
Keyword(s):  
Copyright Protection ◽  
Extraction Method ◽  
Video Sequence ◽  
Characteristic Difference ◽  
Original Sequence ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Frame Sequence ◽  
Frame Difference ◽  
Key Frames

The paper proposes a key frame extraction method for video copyright protection. The fast and robust method is based on frame difference with low level features, including color feature and structure feature. A two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Compared with the previous methods, the proposed method has advantage in computation complexity and robustness on several video formats, video resolution, and so on.

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