Key Frame Extraction from Video

2014 ◽  
Vol 4 (2) ◽  
pp. 68-79 ◽  
Author(s):  
Sergii Mashtalir ◽  
Olena Mikhnova
Keyword(s):  
Artificial Intelligence ◽  
Shot Boundary Detection ◽  
Post Processing ◽  
Extraction Techniques ◽  
Key Frame Extraction ◽  
Estimation Techniques ◽  
Key Frame ◽  
Shot Boundary ◽  
Textual Features ◽  
Key Frames

A complete overview of key frame extraction techniques has been provided. It has been found out that such techniques usually have three phases, namely shot boundary detection as a pre-processing phase, main phase of key frame detection, where visual, structural, audio and textual features are extracted from each frame, then processed and analyzed with artificial intelligence methods, and the last post-processing phase lies in removal of duplicates if they occur in the resulting sequence of key frames. Estimation techniques and available test video collections have been also observed. At the end, conclusions concerning drawbacks of the examined procedure and basic tendencies of its development have been marked.

A Novel Algorithm for Key Frames Selection

2012 ◽  
Vol 182-183 ◽  
pp. 2025-2029
Author(s):  
Chen Kuei Yang ◽  
Shyi Chyi Cheng
Keyword(s):  
Boundary Detection ◽  
Experimental Results ◽  
Shot Boundary Detection ◽  
Color Model ◽  
Frame Selection ◽  
Key Frame ◽  
Shot Boundary ◽  
Key Frame Selection ◽  
Key Frames ◽  
Novel Algorithm

The main objective of this study framework is to help the user to management the interesting video images from the huge storage device. The study can be divided into three main steps is listing as follows: shot boundary detection, key frame selection and evaluation method are designed by analyzing the video content. In this paper we proposed a novel algorithm for shot boundary detection. First, transform the RGB color model into LUV color model according our experimental results. The useful features of each frame are extracted from sequence images by the automatically computation of their color moment-preserving and matching 64-predefined bit planes in order to detect the shot boundary. The excellent shot boundary detection will result in the good key frames selection. Second, the key frame selection is proceed by applying the geometric invariant moments which successful be used in target tracing, pattern recognition, and facial recognition. In our experiments we used five test sequences that belong to five different program categories: movies, advertising, sport game, news, and commercial documentary. The experimental results showed that our method is feasible, effective, and is better than the former approaches.

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.

A Novel Framework for Efficient Extraction of Meaningful Key Frames From Surveillance Video

2019 ◽  
pp. 342-359
Author(s):  
Suresh Chandra Raikwar ◽  
Charul Bhatnagar ◽  
Anand Singh Jalal
Keyword(s):  
Computational Cost ◽  
Extraction Methods ◽  
Surveillance Video ◽  
Distribution Method ◽  
Color Distribution ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Efficient Extraction ◽  
Key Frames ◽  
Edge Histogram

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.

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.

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