Interest-Oriented Video Summarization with Keyframe Extraction

2019 ◽  
Author(s):  
Pawara Gunawardena ◽  
Heshan Sudarshana ◽  
Oshada Amila ◽  
Rashmika Nawaratne ◽  
Damminda Alahakoon ◽  
...  
Keyword(s):  
Video Summarization ◽  
Keyframe Extraction

An Improved Algorithm of Keyframe Extraction for Video Summarization

2011 ◽  
Vol 225-226 ◽  
pp. 807-811
Author(s):  
Zhong Qu ◽  
Teng Fei Gao
Keyword(s):  
Video Segmentation ◽  
Video Retrieval ◽  
Video Summarization ◽  
Color Space ◽  
Original Video ◽  
Video Content ◽  
Hsv Color Space ◽  
Key Frame ◽  
Keyframe Extraction ◽  
Histogram Intersection

Video segmentation and keyframe extraction are the basis of Content-based Video Retrieval (CBVR), in which keyframe selection plays the central role in CBVR. In this paper, as the initialization of keyframe extraction, we proposed an improved approach of key-frame extraction for video summarization. In our approach, videos were firstly segmented into shots according to video content, by our improved histogram-based method, with the use of histogram intersection and nonuniform partitioning and weighting. Then, within each shot, keyframes were determined with the calculation of image entropy as a reflection of the quantity of image information in HSV color space of every frame. Our simulation results in section 4 prove that extracted key frames with our method are compact and faithful to the original video.

Static Shot based Keyframe Extraction for Multimedia Event Detection

2016 ◽  
Vol 6 (1) ◽  
pp. 28-40
Author(s):  
S. Kaavya ◽  
G. G. Lakshmi Priya
Keyword(s):  
Video Processing ◽  
Video Summarization ◽  
Computational Cost ◽  
Feature Representation ◽  
Divide And Conquer ◽  
Scale Invariant ◽  
Multimedia Event Detection ◽  
Keyframe Extraction ◽  
Fidelity Measure ◽  
Scale Invariant Feature

Nowadays, processing of Multimedia information leads to high computational cost due its larger size especially for video processing. In order to reduce the size of the video and to save the user's time in spending their attention on whole video, video summarization is adopted. However, it can be performed using keyframe extraction from the video. To perform this task, a new simple keyframe extraction method is proposed using divide and conquer strategy in which, Scale Invariant Feature Transform (SIFT) based feature representation vector is extracted and the whole video is categorized into static and dynamic shots. The dynamic shot is further processed till it becomes static. A representative frame is extracted from every static shot and the redundant keyframes are removed using keyframe similarity matching measure. Experimental evaluation is carried out and the proposed work is compared with related existing work. The authors' method outperforms existing methods in terms of Precision (P), Recall (R), F-Score (F). Also, Fidelity measure is computed for proposed work which gives better result.

scholarly journals Review of Keyframe Extraction Techniques for Video Summarization

2018 ◽  
Vol 180 (39) ◽  
pp. 40-43
Author(s):  
Akshay Deshpande ◽  
Vedang Bamnote ◽  
Bhakti Patil ◽  
Ashvini A.
Keyword(s):  
Video Summarization ◽  
Extraction Techniques ◽  
Keyframe Extraction

scholarly journals Video Summarization using Keyframe Extraction Methods

2020 ◽  
Vol 9 (2) ◽  
pp. 1030-1032
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Compression ◽  
Video Summarization ◽  
Extraction Methods ◽  
Video Browsing ◽  
Keyframe Extraction ◽  
Proposed Model ◽  
Static Video Summarization ◽  
Fundamental Units

Video summarization plays an important role in too many fields, such as video indexing, video browsing, video compression, video analyzing and so on. One of the fundamental units in the video structure analysis is the keyframe extraction, Keyframe provides meaningful frames from the video. The keyframe consists of the meaningful frame from the videos which help for video summarization. In this proposed model, we presented an approach that is based on Convolutional Neural Network, keyframe extraction from videos and static video summarization. First, the video should be converted to frames. Then we perform redundancy elimination techniques to reduce the redundancy from frames. Then extract the keyframes from video by using the Convolutional Neural Network(CNN) model. From the extracted keyframe, we form a video summarization.

Video Summarization by Curve Simplification

1998 ◽  
Author(s):  
Daniel DeMenthon ◽  
Vikrant Kobla ◽  
David Doermann
Keyword(s):  
Video Summarization ◽  
Curve Simplification
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