Differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval

2021 ◽  
pp. 9-17
Author(s):  
Abhishek Dixit ◽  
Ashish Mani ◽  
Rohit Bansal
Keyword(s):  
Differential Evolution ◽  
Video Retrieval ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Shot Boundary Detection ◽  
Shot Boundary ◽  
Content Based Video Retrieval

scholarly journals An Efficient and Robust Temporal Video Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 4332-4337
Keyword(s):  
Video Segmentation ◽  
Video Retrieval ◽  
Boundary Detection ◽  
Detection Methods ◽  
Shot Boundary Detection ◽  
Feature Descriptor ◽  
Local Contrast ◽  
Temporal Video Segmentation ◽  
Shot Boundary ◽  
Content Based Video Retrieval

Temporal video segmentation is the primary step of content based video retrieval. The whole processes of video management are coming under the focus of content based video retrieval, which includes, video indexing, video retrieval, and video summarization etc. In this paper, we proposed a computationally efficient and discriminating shot boundary detection method, which uses a local feature descriptor named local Contrast and Ordering (LCO) for feature extraction. The results of the experiments, which are conducted on the video dataset TRECVid, analyzed and compared with some existing shot boundary detection methods. The proposed method has given a promising result, even in the cases of illumination changes, rotated images etc.

Performance Evaluation and Scheme Selection of Shot Boundary Detection and Keyframe Extraction in Content-Based Video Retrieval

2017 ◽  
Vol 9 (4) ◽  
pp. 15-29
Author(s):  
Lingchen Gu ◽  
Ju Liu ◽  
Aixi Qu
Keyword(s):  
Video Retrieval ◽  
Color Space ◽  
Boundary Detection ◽  
Extraction Methods ◽  
Video Data ◽  
Shot Boundary Detection ◽  
Threshold Method ◽  
Keyframe Extraction ◽  
Shot Boundary ◽  
Content Based Video Retrieval

The advancement of multimedia technology has contributed to a large number of videos, so it is important to know how to retrieve information from video, especially for crime prevention and forensics. For the convenience of retrieving video data, content-based video retrieval (CBVR) has got great publicity. Aiming at improving the retrieval performance, we focus on the two key technologies: shot boundary detection and keyframe extraction. After being compared with pixel analysis and chi-square histogram, histogram-based method is chosen in this paper. Then we combine it with adaptive threshold method and use HSV color space to get the histogram. For keyframe extraction, four methods are analyzed and four evaluation criteria are summarized, both objective and subjective, so the opinion is finally given that different types of keyframe extraction methods can be used for varied types of videos. Then the retrieval can be based on keyframes, simplifying the process of video investigation, and helping criminal investigation personnel to improve work efficiency.

A Shot Boundary Detection Method Based on PSO-SVM

2011 ◽  
Vol 130-134 ◽  
pp. 3821-3825 ◽  
Author(s):  
Long Zhao ◽  
Xue Mei Sun ◽  
Ming Wei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Video Retrieval ◽  
Particle Swarm ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Video Data ◽  
Shot Boundary Detection ◽  
Support Vector ◽  
Swarm Optimization ◽  
Shot Boundary

Shot boundary detection (SBD) is the first step which segments video data into elementary shots for content-based video retrieval. In this paper, a shot boundary detection algorithm based on support vector machine (SVM) and particle swarm optimization (PSO) is proposed. First of all, the extracted features of pixel domain and compressed domain are combined to form a multi-dimension feature vector by using the scheme of sliding window. Next, particle swarm optimization with global search capacity is adopted to seek the approximately optimal parameters of radial basis function of SVM. Finally the model trained by the parameters obtained is applied to judge and categorize the frames into cut transitions, gradual transitions and non-transitions. The experimental results on the TREC video set 2001 demonstrate our algorithm is efficient and robust, and it solves the difficulty in parameter selection of SVM well.

Rapid Shot Boundary Detection Algorithm Based on Rough Sets in Video Compressed Domain

2009 ◽  
Vol 1 (2) ◽  
pp. 70-78 ◽  
Author(s):  
L. Xiang-Wei ◽  
L. Zhan-Ming ◽  
Z. Ming-Xin ◽  
Z. Ya-lLn ◽  
W. Wei-Yi
Keyword(s):  
Rough Sets ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Shot Boundary Detection ◽  
Compressed Domain ◽  
Shot Boundary

scholarly journals Shot Boundary Detection Algorithm Based on Multi-Feature Fusion

2013 ◽  
Vol 347-350 ◽  
pp. 3866-3871
Author(s):  
Kai Jin ◽  
Hong Cai Feng ◽  
Qi Feng ◽  
Chi Zhang
Keyword(s):  
Feature Fusion ◽  
Texture Feature ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Shot Boundary Detection ◽  
Features Fusion ◽  
Orientation Histogram ◽  
Video Shot ◽  
Shot Boundary ◽  
The Ideal

To establish a general and robust shot boundary detection algorithm, according to characteristics of lens conversion and the ideal of multiple video features fusion, a shot boundary detection algorithm is proposed based on YUV histogram, texture feature and edge orientation histogram in the paper. Besides, global and self-adaptive threshold are combined to use so as to control the process of shot boundary detection and enhance the accuracy of threshold selection. The experiment results show that the algorithm can effectively realize video shot boundary detection and strengthen the robustness of the detection.

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