Key Frame Extraction Techniques

2018 ◽  
pp. 342-345
Author(s):  
Mie Mie Khin ◽  
Zin Mar Win ◽  
Phyo Phyo Wai ◽  
Khaing Thazin Min
Keyword(s):  
Extraction Techniques ◽  
Key Frame Extraction ◽  
Key Frame

Key-Frame Extraction Techniques: A Review

2018 ◽  
Vol 11 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Milan Kumar Asha Paul ◽  
Janakiraman Kavitha ◽  
P. Arockia Jansi Rani
Keyword(s):  
Extraction Techniques ◽  
Key Frame Extraction ◽  
Key Frame

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.

The Research of Key Frame Extraction Technique Based on Video Content

2011 ◽  
Vol 474-476 ◽  
pp. 760-763
Author(s):  
Wei Zhe
Keyword(s):  
Video Analysis ◽  
Video Retrieval ◽  
Extraction Technique ◽  
Video Content ◽  
Extraction Techniques ◽  
Extraction Technology ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Typical Method ◽  
Made In

Key frame extraction is the precondition and fundamental of the video retrieval and video analysis. The method of key frame extraction determines the final result of video analysis. Firstly, the paper introduced the basic theories and principles of the Key frame extraction technology. Secondly, examined the typical method of key frame extraction techniques of video based on non-compression domain and compression-domain in detailed, at the same time, the evaluation and comparisons of the methods are made by experimental. Thirdly, the summarization is given and the prospection of Key frame extraction in the future is made in the end of paper.

scholarly journals Large-Scale Video Retrieval via Deep Local Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chen Zhang ◽  
Bin Hu ◽  
Yucong Suo ◽  
Zhiqiang Zou ◽  
Yimu Ji
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Video Data ◽  
Query Image ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Storage Cost ◽  
Extraction Algorithm ◽  
Feature Aggregation ◽  
And Storage

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

Sign in / Sign up

Export Citation Format

Share Document