{"title":"Shot boundary detection using texture feature based on co-occurrence matrices","authors":"Brojeshwar Bhowmick, Debaleena Chattopadhyay","doi":"10.1109/MSPCT.2009.5164201","DOIUrl":null,"url":null,"abstract":"Content based video indexing and retrieval traces back to the elementary video structures, such as a table of contents. Thus, algorithms for video partitioning have become crucial with the unremitting growth in the prevalent digital video technology. This demands for a tool which would break down the video into smaller and manageable units called shots. In this paper, a shot boundary detection technique has been proposed for abrupt scene cuts. The method computes cooccurrence matrices by taking block differences between the consecutive frames in each of R, G, and B plane, using sum of absolute differences (SAD). Feature vectors are extracted from the co-occurrence matrices' statistics, defined at various pixel displacement distances. The statistical find-outs are integrated into a training set and an unsupervised classifier, K-means, is used to identify the shot-frames and the non-shot frames.","PeriodicalId":179541,"journal":{"name":"2009 International Multimedia, Signal Processing and Communication Technologies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Multimedia, Signal Processing and Communication Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSPCT.2009.5164201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Content based video indexing and retrieval traces back to the elementary video structures, such as a table of contents. Thus, algorithms for video partitioning have become crucial with the unremitting growth in the prevalent digital video technology. This demands for a tool which would break down the video into smaller and manageable units called shots. In this paper, a shot boundary detection technique has been proposed for abrupt scene cuts. The method computes cooccurrence matrices by taking block differences between the consecutive frames in each of R, G, and B plane, using sum of absolute differences (SAD). Feature vectors are extracted from the co-occurrence matrices' statistics, defined at various pixel displacement distances. The statistical find-outs are integrated into a training set and an unsupervised classifier, K-means, is used to identify the shot-frames and the non-shot frames.
基于内容的视频索引和检索可以追溯到基本的视频结构,如目录。因此,随着数字视频技术的不断发展,视频分割算法变得至关重要。这就需要一种工具将视频分解成更小的、可管理的单元,称为镜头。本文提出了一种针对场景突然剪切的镜头边界检测技术。该方法采用绝对差和(sum of absolute difference, SAD)的方法,取R、G、B各平面连续帧之间的块差来计算共发生矩阵。特征向量是从共现矩阵的统计量中提取的,这些统计量在不同的像素位移距离上定义。统计结果被整合到训练集中,并使用无监督分类器K-means来识别投篮帧和非投篮帧。