Video Shot Boundary Detection for Video Indexing

Author(s):  
Waleed E. Farag ◽  
Hussein Abdel-Wahab

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.

Author(s):  
Rashmi B S ◽  
Nagendraswamy H S

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.


Author(s):  
Mohammad A. Al-Jarrah ◽  
Faruq A. Al-Omari

A video is composed of set of shots, where shot is defined as a sequence of consecutive frames captured by one camera without interruption. In video shot transition could be a prompt (hard cut) or gradual (fade, dissolve, and wipe). Shot boundary detection is an essential component of video processing. These boundaries are utilized on many aspect of video processing such as video indexing, and video in demand. In this paper, the authors proposed a new shot boundary detection algorithm. The proposed algorithm detects all type of shot boundaries in a high accuracy. The algorithm is developed based on a global stochastic model for video stream. The proposed stochastic model utilizes the joined characteristic function and consequently the joined momentum to model the video stream. The proposed algorithm is implemented and tested against different types of categorized videos. The proposed algorithm detects cuts fades, dissolves, and wipes transitions. Experimental results show that the algorithm has high performance. The computed precision and recall rates validated its performance.


Author(s):  
Hong Lu ◽  
Zhenyan Li ◽  
Yap-Peng Tan ◽  
Xiangyang Xue

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.


Author(s):  
Nikos Nikolaidis ◽  
Costas Cotsaces ◽  
Zuzana Cernekova ◽  
Ioannis Pitas

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