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首页> 外文期刊>Journal of visual communication & image representation >A new pyramidal opponent color-shape model based video shot boundary detection
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A new pyramidal opponent color-shape model based video shot boundary detection

机译：基于新的金字塔对手颜色形状模型的视频拍边界检测

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摘要

Video shot boundary detection (VSBD) is one of the most essential criteria for many intelligent video analysis-related applications, such as video retrieval, indexing, browsing, categorization and summarization. VSBD aims to segment big video data into meaningful fragments known as shots. This paper put forwards a new pyramidal opponent colour-shape (POCS) model which can detect abrupt transition (AT) and gradual transition (GT) simultaneously, even in the presence of illumination changes, huge object movement between frames, and fast camera motion. First, the content of frames in the video subjected to VSBD is represented by the proposed POCS model. Consequently, the temporal nature of the POCS model is subjected to a suitable segment (SS) selection procedure in order to minimize the complexity of VSBD method. The SS from the video frames is examined for transitions within it using a bagged-trees classifier (BTC) learned on a balanced training set via parallel processing. To prove the superiority of the proposed VSBD algorithm, it is evaluated on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets for classifying the basic units of video according to no transition (NT), AT and GT. The experimental evaluation results in an F-l -score of 95.13%, 98.13% and 97.11% on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets, respectively. (C) 2020 Elsevier Inc. All rights reserved.
机译：视频拍边界检测（VSBD）是许多智能视频分析相关应用的最重要标准之一，例如视频检索，索引，浏览，分类和摘要。 VSBD旨在将大视频数据分段为称为镜头的有意义的片段。本文提出了一种新的金字塔对手颜色形状（POC）模型，可以同时检测突变转变（处）和逐渐过渡（GT），即使在存在照明变化，帧之间的巨大物体运动和快速相机运动也是如此。首先，经过VSBD的视频中的帧的内容由所提出的POCS模型表示。因此，对POCS模型的时间性进行适当的区段（SS）选择过程，以最小化VSBD方法的复杂性。视频帧中的SS用于使用袋装树分类器（BTC）在其上通过并行处理设置的平衡训练中学习的转换。为了证明所提出的VSBD算法的优越性，它在TRECVID 2001，TRECVID2007和VideoSeG2004数据集上进行评估，用于根据没有转换（NT），AT和GT来对视频的基本单位进行分类。实验评估分别导致TRECVID 2001，TRECVID2007和VideoseG2004数据集的95.13％，98.13％和97.11％的F-L-L-L-L-L-1。 （c）2020 Elsevier Inc.保留所有权利。

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