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.

Effective Video Shot Boundary Detection and Keyframe Selection using Soft Computing Techniques

2018 ◽  
Vol 8 (2) ◽  
pp. 27-48 ◽  
Author(s):  
Rashmi B S ◽  
Nagendraswamy H S
Keyword(s):  
Fuzzy Sets ◽  
Video Analysis ◽  
Boundary Detection ◽  
Extraction Methods ◽  
Video Data ◽  
Shot Boundary Detection ◽  
Keyframe Extraction ◽  
Soft Computing Techniques ◽  
Video Shot ◽  
Shot Boundary

The amount of video data generated and made publicly available has been tremendously increased in today's digital era. Analyzing these huge video repositories require effective and efficient content-based video analysis systems. Shot boundary detection and Keyframe extraction are the two major tasks in video analysis. In this direction, a method for detecting abrupt shot boundaries and extracting representative keyframe from each video shot is proposed. These objectives are achieved by incorporating the concepts of fuzzy sets and intuitionistic fuzzy sets. Shot boundaries are detected using coefficient of correlation on fuzzified frames. Further, probabilistic entropy measures are computed to extract the keyframe within fuzzified frames of a shot. The keyframe representative of a shot is the frame with highest entropy value. To show the efficacy of the proposed methods two benchmark datasets are used (TRECVID and Open Video Project). The proposed methods outperform when compared with some of state-of-the-art shot boundary detection and keyframe extraction methods.

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.

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.

scholarly journals Shot Boundary Detection Method Based on a New Extensive Dataset and Mixed Features

2021 ◽  
Author(s):  
Alexander Gushchin ◽  
Anastasia Antsiferova ◽  
Dmitriy Vatolin
Keyword(s):  
Detection Method ◽  
Boundary Detection ◽  
Video Data ◽  
Video Codec ◽  
Shot Boundary Detection ◽  
Test Results ◽  
Scene Change Detection ◽  
Mixed Features ◽  
Color Histograms ◽  
Shot Boundary

Shot boundary detection in video is one of the key stages of video data processing. A new method for shot boundary detection based on several video features, such as color histograms and object boundaries, has been proposed. The developed algorithm was tested on the open BBC Planet Earth [1] and RAI [2] datasets, and the MSU CC datasets, based on videos used in the video codec comparison conducted at MSU, as well as videos from the IBM set, were also plotted. The total dataset for algorithm development and testing exceeded the known TRECVID datasets. Based on the test results, the proposed algorithm for scene change detection outperformed its counterparts with a final F-score of 0.9794.

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