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

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.

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

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2017100102 ◽

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.

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An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram

International Journal of Multimedia Information Retrieval ◽

10.1007/s13735-016-0095-6 ◽

2016 ◽

Vol 5 (2) ◽

pp. 89-104 ◽

Cited By ~ 46

Author(s):

Rachida Hannane ◽

Abdessamad Elboushaki ◽

Karim Afdel ◽

P. Naghabhushan ◽

Mohammed Javed

Keyword(s):

Efficient Method ◽

Boundary Detection ◽

Shot Boundary Detection ◽

Point Distribution ◽

Keyframe Extraction ◽

Distribution Histogram ◽

Video Shot ◽

Shot Boundary

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Video Shot Boundary Detection and Scene Segmentation

Advances in Image and Video Segmentation ◽

10.4018/978-1-59140-753-9.ch009 ◽

2006 ◽

pp. 188-208

Author(s):

Hong Lu ◽

Zhenyan Li ◽

Yap-Peng Tan ◽

Xiangyang Xue

Keyword(s):

Boundary Detection ◽

Video Data ◽

Shot Boundary Detection ◽

Detection Accuracy ◽

Large Set ◽

Scene Segmentation ◽

Research Attention ◽

Segmentation Methods ◽

Video Shot ◽

Shot Boundary

This chapter presents a new and efficient method for shot boundary detection (SBD) and scene segmentation. Commonly the first step in content-based video analysis, SBD partitions video data into fundamental units of shots. Over the past decade, SBD has attracted a considerable amount of research attention. However, the detection accuracy achieved leaves much to be desired. In this chapter, a new SBD method based on sequential change detection is proposed to achieve improved detection accuracy. The method is then extended to segment videos into scenes. Compared with existing scene segmentation methods, the proposed method can also obtain more accurate results over a large set of test videos.

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Video Shot Boundary Detection for Video Indexing

Video Data Management and Information Retrieval ◽

10.4018/978-1-59140-571-9.ch008 ◽

2011 ◽

pp. 193-216

Author(s):

Waleed E. Farag ◽

Hussein Abdel-Wahab

Keyword(s):

Boundary Detection ◽

Video Stream ◽

Video Data ◽

Shot Boundary Detection ◽

Scene Change ◽

Old Dominion University ◽

Video Shot ◽

Detection Stage ◽

Shot Boundary ◽

Analysis System

The increasing use of multimedia streams nowadays necessitates the development of efficient and effective methodologies for manipulating databases storing this information. Moreover, in its first stage, content-based access to video data requires parsing of each video stream into its building blocks. The video stream consists of a number of shots, each one a sequence of frames pictured using a single camera. Switching from one camera to another indicates the transition from a shot to the next one. Therefore, the detection of these transitions, known as scene change or shot boundary detection, is the first step in any video-analysis system. A number of proposed techniques for solving the problem of shot boundary detection exist, but the major criticisms to them are their inefficiency and lack of reliability. The reliability of the scene change detection stage is a very significant requirement because it is the first stage in any video retrieval system; thus, its performance has a direct impact on the performance of all other stages. On the other hand, efficiency is also crucial due to the voluminous amounts of information found in video streams. This chapter proposes a new robust and efficient paradigm capable of detecting scene changes on compressed MPEG video data directly. This paradigm constitutes the first part of a Video Content-based Retrieval (VCR) system that has been designed at Old Dominion University. At first, an abstract representation of the compressed video stream, known as the DC sequence, is extracted, then it is used as input to a Neural Network Module that performs the shot boundary-detection task. We have studied experimentally the performance of the proposed paradigm and have achieved higher shot boundary detection and lower false alarms rates than other techniques. Moreover, the efficiency of the system outperforms other approaches by several times. In short, the experimental results show the superior efficiency and robustness of the proposed system in detecting shot boundaries and flashlights — sudden lighting variation due to camera flash occurrences — within video shots.

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