新的时空特征点检测方法

›› 2012, Vol. ›› Issue (03): 754-758.

新的时空特征点检测方法

尹建芹¹, 王晶晶², 李金屏¹

1. 济南大学山东省网络环境智能计算技术重点实验室, 济南 250022;
2. 山东师范大学物理与电子科学学院, 济南 250014

收稿日期:2011-01-11 出版日期:2012-05-01
通讯作者: 王晶晶(1977-),女,讲师,硕士.研究方向:图像处理.E-mail:wangjingjing@163.com E-mail:wangjingjing@163.com
基金资助:
山东省高等学校发展计划项目(J11LG01);济南市高等学校科研计划项目(TNK1005).

New space-time interest point detection scheme based on cumulative entropy difference

YIN Jian-qin¹, WANG Jing-jing², LI Jin-ping¹

1. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China;
2. College of Physics and Electronics, Shandong Normal University, Jinan 250014, China

Received:2011-01-11 Online:2012-05-01

摘要/Abstract

摘要： 提出了基于累积差熵的时空特征点检测算法。首先利用周期性时空检测方法检测视频的关键特征点;然后提出了视频累积差熵的概念,用累积差熵作为特征点的评价准则;以该准则为基础,选择具有累积差熵大的特征点作为关键点,并对关键视频进行聚类,得到关键视频的原型特征。实验结果表明:本文方法可以简单有效地去除非运动信息得到的关键点,可以较好地用于动作识别、表情识别等视频分析领域。

关键词: 信息处理技术, 时空特征点, 熵, 视频分析, 动作识别

Abstract: A new scheme to detect space-time interest points based on cumulative entropy difference is proposed for human action recognition and video analysis. First, periodic space-time features are detected. Second, the concept of cumulative entropy difference is put forward as the criterion to evaluate the interest points. Third, the points with high cumulative entropy are selected as the final key points. Finally, c-means cluster analysis is applied to find the features, and the prototype of the key video is obtained. The video is converted to the feature vector presented by the prototype; then the video classification is conducted. Experimental results show that the cumulative entropy difference evaluation can effectively remove the noise cuboids, and the method based on the evaluation can realize human action analysis and facial expression recognition.

Key words: information processing, space-time interest points, entropy, action recognition, video analysis

中图分类号:

TN911.73

尹建芹, 王晶晶, 李金屏. 新的时空特征点检测方法 [J]. , 2012, (03): 754-758.

YIN Jian-qin, WANG Jing-jing, LI Jin-ping. New space-time interest point detection scheme based on cumulative entropy difference[J]. , 2012, (03): 754-758.

参考文献

[1] Harris C, Stephens M. A combined corner and edge detector［C］//Proceedings of The Fourth Alvey Vision Conference, 1988: 147-151.
[2] Mikolajczyk K, Schmid C. Indexing based on scale invariant interest points［C］//Proceedings. Eighth IEEE International Conference on Computer Vision, Vancouver BC, Canada,2001:525-531.
[3] Lowe D G. Distinctive image features from scale-invariant keypoints［J］.International Journal of Computer Vision, 2004,60(2): 91-110.
[4] 刘萍萍, 赵宏伟, 耿庆田,等. 基于局部特征和视皮层识别机制的图像分类［J］. 吉林大学学报:工学版,2011, 41 (5):1401-1406. Liu Ping-ping, Zhao Hong-wei, Geng Qing-tian,et al. Image classification method based on local feature and visual cortex recognition mechanism［J］. Journal of Jilin University(Engineering and Technology Edition), 2011, 41 (5):1401-1406.
[5] Laptev I, Lindeberg T. Space-time interest points［C］//ICCV, France,2003:432-439.
[6] Laptev I, Lindeberg T. Interest point detection and scale selection in space-time［C］//In Proc Scale Space Methods in Computer Vision, Isle of Skye, UK,2003:372-387.
[7] Laptev I. On space-time interest points［J］. International Journal of Computer Vision,2005,64(2/3):107-123.
[8] Laptev I, Caputo B,Schüldt C, et al. Local velocity-adapted motion events for spatio-temporal recognition［J］. Computer Vision and Image Understanding,2007,108(3):207-229.
[9] Imran N, Dexter E, Laptev I. View-independent action recognition form temporal self-similarities［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,31(1):172-185.
[10] Dollar P, Rabaud V, Cottrell G,et al. Behavior recognition via sparse spatio-temporal features［C］//Proceedings of the 14th International Conference on Computer Communications and Networks, Washington DC, USA,2005:65-72.
[11] Belongie S, Branson K, Dollar P. Monitoring animal behavior in the smart vivarium［C］//Measuring Behavior,2005:72-75.
[12] Ke Y, Sukthankar R, Hebert M,et al. Efficient visual event detection using volumetric features［C］//Tenth IEEE International Conference on Computer Vision, Beijing, China,2005:166-173.
[13] Oikonomopoulos A, Patras I, Pantic M. Human action recognition with spatiotemporal salient points［J］. IEEE Transactions on Systems, Man, and Cybernetics,2006,36(3):710-719.
[14] Bregonzio M, Gong S G,Xiang T. Recognising action as clouds of space-time interest points［C］//Computer Vision and Pattern Recognition, Miami FL,2009:1948-1955.
[15] Willems G, Tuytelaars T,Gool L,et al. An efficient dense and scale-invariant spatio-temporal interest point detector［C］//Proceedings of the 10th European Conference on Computer Vision: Part II, Berlin, Heidelberg,2008:650-663.
[16] Wong Shu-fai, Roberto C. Extracting spatiotemporal interest points using global information［C］//IEEE 11th International Conference on Computer Vision, Rio de Janeiro,2007:1-8.

相关文章 15

[1]	苏寒松,代志涛,刘高华,张倩芳. 结合吸收Markov链和流行排序的显著性区域检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1887-1894.
[2]	徐岩,孙美双. 基于卷积神经网络的水下图像增强方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1895-1903.
[3]	黄勇,杨德运,乔赛,慕振国. 高分辨合成孔径雷达图像的耦合传统恒虚警目标检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1904-1909.
[4]	李居朋,张祖成,李墨羽,缪德芳. 基于Kalman滤波的电容屏触控轨迹平滑算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1910-1916.
[5]	应欢,刘松华,唐博文,韩丽芳,周亮. 基于自适应释放策略的低开销确定性重放方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1917-1924.
[6]	陆智俊,钟超,吴敬玉. 星载合成孔径雷达图像小特征的准确分割方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1925-1930.
[7]	刘仲民,王阳,李战明,胡文瑾. 基于简单线性迭代聚类和快速最近邻区域合并的图像分割算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1931-1937.
[8]	单泽彪,刘小松,史红伟,王春阳,石要武. 动态压缩感知波达方向跟踪算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1938-1944.
[9]	念腾飞, 李萍, 林梅. 冻融循环下沥青特征官能团含量与流变参数灰熵分析及微观形貌[J]. 吉林大学学报(工学版), 2018, 48(4): 1045-1054.
[10]	姚海洋, 王海燕, 张之琛, 申晓红. 双Duffing振子逆向联合信号检测模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[11]	全薇, 郝晓明, 孙雅东, 柏葆华, 王禹亭. 基于实际眼结构的个性化投影式头盔物镜研制[J]. 吉林大学学报(工学版), 2018, 48(4): 1291-1297.
[12]	陈绵书, 苏越, 桑爱军, 李培鹏. 基于空间矢量模型的图像分类方法[J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[13]	陈涛, 崔岳寒, 郭立民. 适用于单快拍的多重信号分类改进算法[J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[14]	孟广伟, 李荣佳, 王欣, 周立明, 顾帅. 压电双材料界面裂纹的强度因子分析[J]. 吉林大学学报(工学版), 2018, 48(2): 500-506.
[15]	林金花, 王延杰, 孙宏海. 改进的自适应特征细分方法及其对Catmull-Clark曲面的实时绘制[J]. 吉林大学学报(工学版), 2018, 48(2): 625-632.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed