Shot segmentation technology based on SURF features and SIFT features

Hao-su ZHANG; Xiao-long ZHU; Xin-zhou HU; Hong-e REN

doi:10.3788/YJYXS20193405.0521

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Shot segmentation technology based on SURF features and SIFT features

Image Processing | 更新时间：2020-07-05

- Shot segmentation technology based on SURF features and SIFT features
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 5, Pages: 521-529(2019)
- 作者机构：
  
  1.东北林业大学信息与计算机工程学院, 黑龙江哈尔滨 150040
  2.黑龙江省林业智能装备工程研究中心, 黑龙江哈尔滨 150040
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193405.0521
  CLC： TP391.41
- Received：25 December 2018，
  
  Accepted：12 March 2019，
  
  Published：05 May 2019
- 稿件说明：
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Hao-su ZHANG, Xiao-long ZHU, Xin-zhou HU, et al. Shot segmentation technology based on SURF features and SIFT features[J]. Chinese journal of liquid crystals and displays, 2019, 34(5): 521-529.
DOI：

Hao-su ZHANG, Xiao-long ZHU, Xin-zhou HU, et al. Shot segmentation technology based on SURF features and SIFT features[J]. Chinese journal of liquid crystals and displays, 2019, 34(5): 521-529. DOI： 10.3788/YJYXS20193405.0521.

摘要

在视频镜头分割处理中，SIFT（Scale-invariant feature transform）特征由于其具有尺度、旋转不变性等诸多优点而被广泛应用，但是SIFT特征提取复杂，计算量大，导致程序效率低下，很难满足实时性的要求。本文提出了一种基于SURF（Speeded Up Robust Features）和SIFT特征的视频镜头分割算法。首先对相关帧进行颜色直方图特征提取，然后通过一个自适应阈值判断是否为候选帧，如果不是，则融合SURF特征再进行复检。最后筛选出的候选帧再通过SIFT算法进行复选，去除误检帧，有效解决了SIFT特征低效率的问题。实验结果表明，对于切变镜头查全率为95%，查准率为93%，对于渐变镜头查全率为90%，查准率为76%，平均单帧运行时间为0.26 s，本算法与SIFT、SURF等单一特征的算法相比，在高效性和准确性之间取得了平衡，并且它是鲁棒的。

Abstract

In the field of video shot segmentation processing

SIFT features are widely used due to their many advantages such as scale and rotation invariance. However

SIFT feature extraction is complex and computationally intensive

resulting in low program efficiency and hard to achieve the requirement of real-time. In this case

an algorithm based on SURF features and SIFT features is proposed in this paper. The algorithm first performs color histogram feature extraction on the frame

determines whether it is a candidate frame through an adaptive threshold

and if not

fuses the SURF feature for re-detection. The candidate frames that are filtered out are then checked by the SIFT algorithm. This can effectively solve the problem of SIFT feature inefficiency. Experimental results show that the recall rate is 95% for the shear shots

93% for the precision

90% for the gradual shots

76% for the precision and 0.26 s for the average single frame running time and compared with SIFT

SURF and other algorithms with a single feature

this algorithm achieves a balance between efficiency and accuracy

and has better robustness.

关键词

Keywords

references

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