DC programming for solving a sparse modeling problem of video key frame extraction

2018 ◽  
Vol 83 ◽  
pp. 214-222 ◽  
Author(s):  
Benying Tan ◽  
Yujie Li ◽  
Shuxue Ding ◽  
Incheon Paik ◽  
Atsunori Kanemura
Keyword(s):  
Dc Programming ◽  
Sparse Modeling ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Modeling Problem

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.

Key Frame Extraction Based on Frame Difference and Cluster for Person Re-identification

2021 ◽  
Author(s):  
Yiyin Ding ◽  
Shaoqi Hou ◽  
Xu Yang ◽  
Wenyi Du ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Key Frame Extraction ◽  
Key Frame ◽  
Frame Difference
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