基于两阶段投票的小样本目标检测方法

doi:10.11772/j.issn.1001-9081.2014.04.1126

计算机应用 ›› 2014, Vol. 34 ›› Issue (4): 1126-1129.DOI: 10.11772/j.issn.1001-9081.2014.04.1126

基于两阶段投票的小样本目标检测方法

徐培¹,赵雪专²,唐红强¹,占伟鹏¹

1. 电子科技大学计算机科学与工程学院,成都 611731;
2. 中国科学院成都计算机应用研究所,成都 610041

收稿日期:2013-10-22 修回日期:2013-12-20 出版日期:2014-04-01 发布日期:2014-04-29
通讯作者: 徐培
作者简介:徐培(1986-),男,四川自贡人,博士研究生,主要研究方向:计算机视觉、机器学习;
赵雪专(1986-),男,河南濮阳人,博士研究生,主要研究方向:计算机视觉、机器学习;
唐红强(1987-),男,安徽安庆人,硕士研究生,主要研究方向:计算机视觉;
占伟鹏(1990-),男,江西九江人,硕士研究生,主要研究方向:计算机视觉。
基金资助:
中央基本业务经费资助项目

Object detection method of few samples based on two-stage voting

XU Pei¹,ZHAO Xuezhuan²,TANG Hongqiang¹,ZHAN Weipeng¹

1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, China
2. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China

Received:2013-10-22 Revised:2013-12-20 Online:2014-04-01 Published:2014-04-29
Contact: XU Pei

摘要/Abstract

摘要：

为了在只有几个样本的情况下在待检测图像中采用模板匹配的方式定位目标，提出了一种两阶段投票的目标检测方法。首先采用概率模型通过几个样本离线构造投票空间，然后采用两阶段投票的方式在待测图像中检测目标：第一阶段在待测图像中通过投票检测目标的图像块，并记录下这些部件块在样本中所处的位置信息；第二阶段基于第一阶段所得到的图像块来投票计算样本整体的相似度，从而定位目标。理论推导和实验结果验证了所提出方法比前人的工作具有更低的时间复杂度和更高的目标检测准确率。

Abstract:

A method of object detection with few samples based on two-stage voting was proposed to detect objects using template matching method while there are only a few samples. Firstly, the voting space was constructed off-line by using probability model through several samples. Then, a method of two-stage voting was used to detect objects in testing images. In the first stage, the components of object from testing image were detected, and the positions of components in query image were saved. In the second stage, the similarity of the object was computed integrally based on the components. According to the theory analysis and experimental results, the proposed method obtains lower computation complexity and higher precisions than previous works.

中图分类号:

TP391.41

徐培赵雪专唐红强占伟鹏. 基于两阶段投票的小样本目标检测方法[J]. 计算机应用, 2014, 34(4): 1126-1129.

XU Pei ZHAO Xuezhuan TANG Hongqiang ZHAN Weipeng. Object detection method of few samples based on two-stage voting[J]. Journal of Computer Applications, 2014, 34(4): 1126-1129.

参考文献

［1］AN S, PEURSUM P, LIU W, et al.Efficient algorithms for subwindow search in object detection and localization ［C］// CVPR 2009: Proceedings of the 2009 IEEE Conference on Computer Vision and Patter Recognition. Piscataway: IEEE, 2009: 264-271.
［2］TIAN Y, TIAN S, XU Y, et al.Image object detection based on local feature and sparse representation ［J］. Journal of Computer Applications, 2013, 33(6): 1670-1673. (田元荣,田松,许悦雷,等.基于局部特征和稀疏表示的图像目标检测算法［J］.计算机应用,2013,33(6): 1670-1673.)
［3］YUAN F. Codebook generation based on self-organizing incremental neural network for image classification ［J］. Journal of Computer Applications, 2013, 33(7): 1976-1979. (袁飞云.基于自组织增量神经网络的码书产生方法在图像分类中的应用［J］.计算机应用,2013,33(7): 1976-1979.)
［4］LEHMANN A, LEIBE B, GOOL L. Feature centric efficient subwindow search ［C］// CVPR 2009: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 940-947.
［5］MUTCH J, LOWE D G, Multiclass object recognition with sparse, localized features ［C］// CVPR 2006: Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2006: 11-18.
［6］BRUNELLI R. Template matching techniques in computer vision: theory and practice ［M］. Hoboken: John Wiley and Sons, 2009.
［7］SHECHTMAN E, IRANI M. Matching local self similarities across images and videos ［C］// CVPR 2007: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2007: 1-8.
［8］SIBIRYAKOV A. Fast and high-performance template matchingmethods ［C］// CVPR 2008: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2008: 1-8.
［9］SEO H J, MILANFAR P. Training-free, generic object detection using locally adaptive regression kernels ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1688-1704.
［10］XU P, YE M, LI X, et al.Object detection using voting spaces trained by few samples ［J］. Optical Engineering, 2013, 52(9): 093105.
［11］XU P, YE M. FU M, et al.Object detection based on several samples with training Hough spaces ［C］// CCPR 2012: Chinese Conference of Pattern Recognition, CCIS 321. Berlin: Springer-Verlag, 2012: 235-242.
［12］DALAL N, TRIGGS B. Histograms of oriented gradients for human detection ［C］// CVPR 2005: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2005: 1-8.
［13］FELZENSZWALB P, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model ［C］// CVPR 2008: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2008: 1-8.
［14］TANG D, LIU Y, KIN T-K. Fast pedestrian detection by cascaded random forest with dominant orientation templates ［C］// BMVC 2012: Proceedings of the 2012 British Machine Vision Conference. Nottingham: BMVA Press, 2012: 1-11.
［15］PELE O, WERMAN M. The quadratic-chi histogram distance family ［C］// ECCV 2010: Proceedings of the 2010 European Conference on Computer Vision, LNCS 6312. Berlin: Springer-Verlag, 2010: 749-762.
［16］RAZAVI N, GALL J, GOOL L. Scalable multi-class object detection ［C］// CVPR2011: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2011: 1505-1512.
［17］ZHANG Y, CHEN T. Weakly supervised object recognition and localization with invariant high order features ［C］// BMVC 2010: Proceedings of the 2010 British Machine Vision Conference. Nottingham: BMVA Press, 2010: 47.1-47.11.
［18］GODEC M, ROTH P M, BISCHOF H. Hough-based tracking of non-rigid objects ［C］// CVPR 2011: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2011: 81-88.
［19］BALUJA H, KANADE T. Neural network based face detection ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(1): 22-38.
［20］AGARWAL S, AWAN A, ROTH D. Learning to detection objects in images via a sparse, part-based representation ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11): 1475-1490.
［21］KAPPOR A, WINN J. Located hidden random fields: learning discriminative parts for object detection ［C］// ECCV 2006: Proceedings of the 2006 European Conference on Computer Vision, LNCS 3953. Berlin: Springer-Verlag, 2006: 302-315.

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基于两阶段投票的小样本目标检测方法

Object detection method of few samples based on two-stage voting

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