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 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 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 Fast Searching Density Peak Clustering Algorithm Based on Shared Nearest Neighbor and Adaptive Clustering Center

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2014
Author(s):  
Yi Lv ◽  
Mandan Liu ◽  
Yue Xiang
Keyword(s):  
Prior Knowledge ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Density Peak ◽  
Adaptive Clustering ◽  
Clustering Center ◽  
Density Peak Clustering ◽  
Shared Nearest Neighbor ◽  
Fast Searching

The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms always need prior knowledge, we proposed a fast searching density peak clustering algorithm based on the shared nearest neighbor and adaptive clustering center (DPC-SNNACC) algorithm. It can automatically ascertain the number of knee points in the decision graph according to the characteristics of different datasets, and further determine the number of clustering centers without human intervention. First, an improved calculation method of local density based on the symmetric distance matrix was proposed. Then, the position of knee point was obtained by calculating the change in the difference between decision values. Finally, the experimental and comparative evaluation of several datasets from diverse domains established the viability of the DPC-SNNACC algorithm.

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 A Data-Driven Parameter Adaptive Clustering Algorithm Based on Density Peak

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Tao Du ◽  
Shouning Qu ◽  
Qin Wang
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Training Data ◽  
Data Driven ◽  
Density Peak ◽  
Data Set ◽  
Adaptive Clustering ◽  
Key Factor ◽  
Parameter Adaptive

Clustering is an important unsupervised machine learning method which can efficiently partition points without training data set. However, most of the existing clustering algorithms need to set parameters artificially, and the results of clustering are much influenced by these parameters, so optimizing clustering parameters is a key factor of improving clustering performance. In this paper, we propose a parameter adaptive clustering algorithm DDPA-DP which is based on density-peak algorithm. In DDPA-DP, all parameters can be adaptively adjusted based on the data-driven thought, and then the accuracy of clustering is highly improved, and the time complexity is not increased obviously. To prove the performance of DDPA-DP, a series of experiments are designed with some artificial data sets and a real application data set, and the clustering results of DDPA-DP are compared with some typical algorithms by these experiments. Based on these results, the accuracy of DDPA-DP has obvious advantage of all, and its time complexity is close to classical DP-Clust.

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