计算机应用 ›› 2009, Vol. 29 ›› Issue (05): 1369-1372.

• 图形图像处理 • 上一篇    下一篇

基于BP神经网络的镜头边界检测

张楠1,肖国强2,江建民3,邱开金1   

  1. 1. 西南大学
    2. 西南大学计算机与信息科学学院
    3. bradford university
  • 收稿日期:2008-11-21 修回日期:2009-01-19 发布日期:2009-06-09 出版日期:2009-05-01
  • 通讯作者: 肖国强
  • 基金资助:
    重庆市自然科学基金项目(项目编号:CSTC-2008BB2252);省部级基金

BPNN algorithm towards shot boundary detection

  • Received:2008-11-21 Revised:2009-01-19 Online:2009-06-09 Published:2009-05-01

摘要: 基于神经网络的机器学习思想,提出一种利用多种视频特征的镜头边界检测算法。突变检测中,在特征矢量形成上,分别采用了相邻两帧差值法和滑动窗口法,并加入运动信息以排除强运动对突变检测的影响;在神经网络的构架上,则分别采用了融合法和选举法。在渐变检测中,先通过三个神经网络将溶解过程中方差曲线的三种模式分别识别出来,再根据溶解过程中亮度均值呈线性递增或递减的特性将干扰排除。对大量TRECVID视频进行实验的结果表明,该算法对视频突变和渐变都具有良好的检测性能,并对运动以及闪光灯的干扰具有较好的鲁棒性。

关键词: 镜头边界检测, 视频分割, BP神经网络, video segmentation, Back Propagation (BP) neural network

Abstract: The authors presented a neural networks based approach of shot boundary detection by using multi-video features. Two approaches, based on feature differences between two adjacent frames and shifting window respectively, were employed to detect abrupt transition, and motion information was used to reduce the influence of strong movement of objects. The fusion and voting techniques were exploited in the final decision stage. In gradual change detection, three patterns of the variance curve of intensity during the dissolving period were distinguished using three neural networks respectively. Then the interference was eliminated according to the characteristics of linear increasing or decreasing of the mean value of intensity during dissolving interval. Experimental results on TRECVID database indicate that the proposed approach works well in detecting shot boundary measured by both recall and precision, and it is also robust to motion and flash light.

Key words: Shot Boundary Detection (SBD)