scholarly journals A Clustering Algorithm for Key Frame Extraction Based on Density Peak

2018 ◽  
Vol 06 (12) ◽  
pp. 118-128 ◽  
Author(s):  
Hong Zhao ◽  
Tao Wang ◽  
Xiangyan Zeng
Keyword(s):  
Clustering Algorithm ◽  
Density Peak ◽  
Key Frame Extraction ◽  
Key Frame

scholarly journals Key-Frame Extraction Based on HSV Histogram and Adaptive Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Zhao ◽  
Wei-Jie Wang ◽  
Tao Wang ◽  
Zhao-Bin Chang ◽  
Xiang-Yan Zeng
Keyword(s):  
Clustering Algorithm ◽  
Fixed Number ◽  
Video Data ◽  
Digital Information ◽  
Density Peak ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Adaptive Clustering ◽  
Digital Information Technology ◽  
Hsv Histogram

Along with the fast development of digital information technology and the application of Internet, video data begins to grow explosively. Some applications with high real-time requirements, such as object detection, require strong online video storage and analysis capabilities. Key-frame extraction is an important technique in video analysis, which provides an organizational framework for dealing with video content and reduces the amount of data required in video indexing. To address the problem, this study proposes a key-frame extraction method based on HSV (hue, saturation, value) histogram and adaptive clustering. The HSV histogram is used as color features for each frame, which reduces the amount of data. Furthermore, by using the transformed one-dimensional eigenvector, the fixed number of features can be extracted for images with different sizes. Then, a cluster validation technique, the silhouette coefficient, is employed to get the appropriate number of clusters without setting any clustering parameters. Finally, several algorithms are compared in the experiments. The density peak clustering algorithm (DPCA) model is shown to be more effective than the other four models in precision and F-measure.

scholarly journals Feature fusion and clustering for key frame extraction

2021 ◽  
Vol 18 (6) ◽  
pp. 9294-9311
Author(s):  
Yunyun Sun ◽  
◽  
Peng Li ◽  
Zhaohui Jiang ◽  
Sujun Hu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Golden Section ◽  
Video Summarization ◽  
Threshold Function ◽  
Cluster Center ◽  
Maximum Weight ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Video Feature

<abstract> <p>Numerous limitations of Shot-based and Content-based key-frame extraction approaches have encouraged the development of Cluster-based algorithms. This paper proposes an Optimal Threshold and Maximum Weight (OTMW) clustering approach that allows accurate and automatic extraction of video summarization. Firstly, the video content is analyzed using the image color, texture and information complexity, and video feature dataset is constructed. Then a Golden Section method is proposed to determine the threshold function optimal solution. The initial cluster center and the cluster number <italic>k</italic> are automatically obtained by employing the improved clustering algorithm. k-clusters video frames are produced with the help of K-MEANS algorithm. The representative frame of each cluster is extracted using the Maximum Weight method and an accurate video summarization is obtained. The proposed approach is tested on 16 multi-type videos, and the obtained key-frame quality evaluation index, and the average of Fidelity and Ratio are 96.11925 and 97.128, respectively. Fortunately, the key-frames extracted by the proposed approach are consistent with artificial visual judgement. The performance of the proposed approach is compared with several state-of-the-art cluster-based algorithms, and the Fidelity are increased by 12.49721, 10.86455, 10.62984 and 10.4984375, respectively. In addition, the Ratio is increased by 1.958 on average with small fluctuations. The obtained experimental results demonstrate the advantage of the proposed solution over several related baselines on sixteen diverse datasets and validated that proposed approach can accurately extract video summarization from multi-type videos.</p> </abstract>

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