基于Jaccard距离与概念聚类的多模型估计

doi:10.3969/j.issn.1000-3428.2012.10.006

计算机工程 ›› 2012, Vol. 38 ›› Issue (10): 22-26.doi: 10.3969/j.issn.1000-3428.2012.10.006

基于Jaccard距离与概念聚类的多模型估计

于永彦 ^1,2

(1. 淮阴工学院计算机工程学院，江苏淮安 223003；2. 河海大学计算机与信息工程学院，南京 210098)

收稿日期:2011-09-19 出版日期:2012-05-20 发布日期:2012-05-20
作者简介:于永彦(1969－)，男，副教授、博士，主研方向：模式识别，多媒体通信
基金项目:
国家“863”计划基金资助项目(2007AA01Z179)

Multi-model Estimation Based on Jaccard Distance and Conceptual Clustering

YU Yong-yan ^1,2

(1. School of Computer Engineering, Huaiyin Institute of Technology, Huaian 223003, China; 2. School of Computer and Information Engineering, Hohai University, Nanjing 210098, China)

Received:2011-09-19 Online:2012-05-20 Published:2012-05-20

摘要/Abstract

摘要： Multi-RANSAC、RHT等方法难以有效实现多模型估计。为此，提出一种基于模型聚类的多模型估计方法。将数据点描述为所属模型的倾向集，把倾向集间的Jaccard距离描述为数据点的一种属性，基于该属性使用改进的Cobweb算法进行聚类。该方法无需预知模型数目和参数变换，可有效克服漏检、交叉模型误检等情况。实验结果表明，该方法具有高效、高精度等优点，适用于消隐点检测、相机自标定等领域。

关键词: 计算机视觉, 外点, 倾向集, Jaccard距离, Cobweb聚类, 多模型估计

Abstract: Multi-RANSAC and RHT these methods are incapable to solve multi-models estimation effectually, and a multi-model estimation method with model-based clustering in conceptual space is proposed. Each data point is represented with a preference set of hypotheses models preferred by that point, and the Jaccard distance between two preference sets is described as a attribute of an data point, to perform a clustering operation using the improved Cobweb algorithm based on the attribute of the data points. Neither this method requires prior specification of the number of models, nor it necessitates parameters transformation, so that it can overcome missing detection and false detection of crossing models. Experimental results show the obvious effect and greater accuracy of the algorithm, thus can be used widely by vanishing point detection, self-calibration of camera, etc.

Key words: computer view, outlier, preference set, Jaccard distance, Cobweb clustering, multi-model estimation

中图分类号:

TP18

于永彦. 基于Jaccard距离与概念聚类的多模型估计[J]. 计算机工程, 2012, 38(10): 22-26.

XU Yong-Pan. Multi-model Estimation Based on Jaccard Distance and Conceptual Clustering[J]. Computer Engineering, 2012, 38(10): 22-26.

参考文献

[1] Wolfson H J, Rigoutsos I. Geometric Hashing: An Overview[J]. IEEE Computational Science & Engineering, 1997, 4(4): 10-21.
[2] Borkar A, Hayes M. Robust Lane Detection and Tracking With RANSAC and Kalman Filter[C]//Proc. of IEEE International Conference on Image Processing. Cairo, Egyp: [s. n.], 2009.
[3] Papalazarou C, Rongen P M J, Peter H N. Multiple Model Estimation for the Detection of Curvilinear Segments in Medical X-ray Images Using Sparse-plus-dense-RANSAC[C]//Proc. of IEEE International Conference on Pattern Recognition. Istanbul, Turkey: [s. n.], 2010.
[4] Black M J, Anandan P. A Framework for the Robust Estimation of Optical Flow[C]//Proc. of EEEE International Conference on Computer Vision. Berlin, Germany: [s. n.], 1993.
[5] Yang Shaowen, Wang Chieh-Chih. Multiple-model RANSAC for Ego-motion Estimation in Highly Dynamic Environments[C]// Proc. of IEEE International Conference on Robotics and Automation. Kobc, Japan: [s. n.], 2009.
[6] Wildenauer H, Vincze M. Vanishing Point Detection in Complex Man-made Worlds[C]//Proc. of International Conference on Image Analysis. Kobc, Japan: [s. n.], 2007.
[7] Hough P V C. Method and Means for Recognizing Complex Patterns: USA, 3069654[P]. 1962-06-20.
[8] 肖富平, 罗军. HT树缓存敏感的内存数据库索引[J]. 计算机工程, 2009, 35(16): 68-70.
[9] 李成. 形状自适应的二维水印方法[J]. 计算机工程, 2007, 33(19): 158-162.
[10] 王华. HT-7超导托卡马克总控数据发布系统研究[J]. 计算机工程, 2007, 33(6): 236-239.
[11] 瞿连政. MDSplus在HT-7Tokamak聚变实验数据系统中的应用[J]. 计算机工程, 2006, 32(6): 64-66.
[12] Hollitt C. Reduction of Computational Complexity of Hough Transforms Using a Convolution Approach[C]//Proc. of International Conference on Image and Vision Computing. Wellington, New Zealand: [s. n.], 2009.
[13] 陈逢林, 胡万宝, 孙广人. 基于超椭圆曲线的顺序多重盲签名[J]. 计算机工程, 2011, 37(9): 160-163.
[14] Satzoda R K, Suchitra S, Srikanthan T. Parallelizing the Hough Transform Computation[J]. IEEE Signal Processing Letters, 2008, 15(1): 297-301.
[15] Xu Lei, Erkki O J A, Kultanena P. A New Curve Detection Method: Randomized Hough Transform(RHT)[J]. Pattern Recognition Letters, 1990, 11(5): 331-338.
[16] Fischler M, Bolles R. Random Sample Consensus. A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[J]. Comm. of the ACM, 1981, 24(6): 381-395.
[17] Vincent E, Laganiere R. Detecting Planar Homographies in an Image Pair[C]//Proc. of International Symposium on Image and Signal Processing and Analysis. Pula, Croatia: [s. n.], 2001.
[18] Kanazawa Y, Kawakami H. Detection of Planar Regions with Uncalibrated Stereo Using Distribution of Feature Points[C]//Proc. of the British Machine Vision Conference. London, UK: [s. n.], 2004.
[19] Zuliani M, Kenney C S, Manjunath B S. The Multi-RANSAC Algorithm and Its Application to Detect Planar Homographies[C]// Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: [s. n.], 2005.
[20] Nister D. Preemptive RANSAC for Live Structure and Motion Estimation[C]//Proc. of International Conference on Computer Vision. Beijing, China: [s. n.], 2003.
[21] Shan Y, Matei B, Sawhney H S, et al. Linear Model Hashing and Batch RANSAC for Rapid and Accurate Object Recognition[C]// Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: [s. n.], 2004.
[22] Matas J, Chum O. Randomized RANSAC with Td; d Test[J]. Image and Vision Computing, 2004, 22(10): 837-842.

基于Jaccard距离与概念聚类的多模型估计

Multi-model Estimation Based on Jaccard Distance and Conceptual Clustering

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