Key frame extraction algorithm for video abstraction applications in underwater videos

2015 ◽  
Author(s):  
N. Sathish Kumar ◽  
G. Shobha ◽  
S. Balaji
Keyword(s):  
Key Frame Extraction ◽  
Key Frame ◽  
Video Abstraction ◽  
Extraction Algorithm

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.

scholarly journals A Meaningful Compact Key Frames Extraction in Complex Video Shots

2017 ◽  
Vol 7 (3) ◽  
pp. 818
Author(s):  
Manar Abduljabbar Mizher ◽  
Mei Choo Ang ◽  
Ahmad Abdel Jabbar Mazhar
Keyword(s):  
State Of The Art ◽  
The State ◽  
Worst Case ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Easy Problem ◽  
Extraction Algorithm ◽  
Key Frames ◽  
High Level ◽  
The Relationship

Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-adaptive threshold (AOF_ST) as recommended in TRIZ inventive principles. Several video shots including original and forgery videos with complex conditions are used to verify the experimental results. The comparison of our results with the-state-of-the-art algorithms results showed that the proposed extraction algorithm can accurately brief the videos and generated a meaningful compact count number of key frames. On top of that, our proposed algorithm achieves 124.4 and 31.4 for best and worst case in KTH dataset extracted key frames in terms of compression rate, while the the-state-of-the-art algorithms achieved 8.90 in the best case.

Key Frame Extraction Algorithm of Sign Language Based on Compressed Sensing and SURF Features

2018 ◽  
Vol 55 (5) ◽  
pp. 051013
Author(s):  
王民 Wang Min ◽  
李泽洋 Li Zeyang ◽  
王纯 Wang Chun ◽  
石新源 Shi Xinyuan
Keyword(s):  
Compressed Sensing ◽  
Sign Language ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Extraction Algorithm ◽  
Surf Features

A Novel Rough Sets Based Key Frame Extraction Algorithm in Compressed-Domain

2008 ◽  
Vol 7 (7) ◽  
pp. 1043-1048 ◽  
Author(s):  
Li Xiang-wei ◽  
Li Zhan-ming ◽  
Zhang Ming-xin ◽  
Wei Zhe ◽  
Zhang Guo-quan
Keyword(s):  
Rough Sets ◽  
Compressed Domain ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Extraction Algorithm
Sign in / Sign up

Export Citation Format

Share Document