SVM Based Shot Boundary Detection Using Block Motion Feature Based on Statistical Moments

被引：1

作者：

Bhowmick, Brojeshwar ^{[1
]}

Goswami, Kaustav ^{[1
]}

机构：

[1] Tata Consultancy Serv Ltd, Innovat Lab, Kolkata, India

来源：

ICAPR 2009: SEVENTH INTERNATIONAL CONFERENCE ON ADVANCES IN PATTERN RECOGNITION, PROCEEDINGS | 2009年

关键词：

temporal video segmentation; shot; cut; block feature; statistical moment; SVM; CUT DETECTION;

D O I：

10.1109/ICAPR.2009.25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Temporal video segmentation is of fundamental importance in order to facilitate user's access to huge volume of video data as well as for video summarization. The objective of shot boundary detection is to partition the video into meaningful, basic structural units called shots. In this paper, a shot boundary detection technique has been proposed for cuts. The method extracts block feature based similarities from the frames of the input video. Statistical moments up to second order are used to measure the motion present in the frames. Feature vectors are generated using a sliding window over time and are trained by a SVM to identify the cuts.

引用

页码：134 / 137

页数：4

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