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WLD: A Robust Local Image Descriptor

WLD: A Robust Local Image Descriptor. Jie Chen, Shiguang Shan, Chu He, Guoying Zhao, Matti Pietikäinen , Xilin Chen, Wen Gao 报告人：蒲薇榄. 作者简介. Jie Chen University of Oulu Senior researcher Department of computer science and engineering Center for Machine Vision Research

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WLD: A Robust Local Image Descriptor

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  1. WLD: A Robust Local Image Descriptor JieChen, Shiguang Shan, Chu He, Guoying Zhao, MattiPietikäinen, Xilin Chen, Wen Gao 报告人：蒲薇榄

  2. 作者简介 Jie Chen • University of Oulu • Senior researcher • Department of computer science and engineering • Center for Machine Vision Research • jie.chen@ee.oulu.fi Biography • His research interests include pattern recognition, computer vision, machine learning, dynamic texture, and watermarking. • He has authored more than 20 papers in journals and conferences, and he is a member of the IEEE.

  3. 作者简介 Shiguang Shan • Chinese Academy of Science • Professor • Institute of computing technology • Key Lab of Intelligent Information Processing(IIP) • sgshan@ict.ac.cn Biography • His research interests cover image analysis, pattern recognition, and computer vision. • He is focusing especially on face recognition related research topics, and has published more than 100 papers on the related research topics.

  4. 作者简介 MattiPietikäinen • University of Oulu • Professor • Department of computer science and engineering • Center for Machine Vision Research • mkp@ee.oulu.fi Biography • His research interests include texture-based computer vision, face analysis, human motion analysis, human-computer interaction, person identification, and visual surveillance. • He has authored more than 250 refereed papers in international journals, books, and conference proceedings, and about 100 other publications or reports. His publications have more than 1,800 citations in the Science Citation Index.

  5. 文章出处 Publication • IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 32, no. 9, September 2010. References • T. Ahonen , A. Hadid and M. Pietikäinen "Face Description with Local Binary Patterns: Application to Face Recognition", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp.2037 -2041 2006 • J. Anderson An Introduction to Neural Networks, 1995 :The MIT Press • J. Begard , N. Allezard and P. Sayd "Real-Time Human Detection in Urban Scenes: Local Descriptors and Classifiers Selection with AdaBoost-like Algorithms", Proc. IEEE Int\'l Conf. Computer Vision and Pattern Recognition Workshops, 2008 • J. Chen , S. Shan , G. Zhao , X. Chen , W. Gao and M. Pietikäinen "A Robust Descriptor Based on Weber\'s Law", Proc. IEEE Int\'l Conf. Computer Vision and Pattern Recognition, 2008 • ……

  6. Abstract Inspired by Weber’s Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. WLD consists of two components: differential excitation and orientation. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on texture classification and human face detection show a promising performance to other widely used descriptors.

  7. 文章结构 • Abstract • Introduction • WLD for Image Representation • Application to Texture Classification • Application to Face Detection • More Experimental Validations and Discussions • Conclusion

  8. 讲解提纲 • 相关知识介绍 • 用于图像描述的WLD算子 • WLD应用于纹理分类 • WLD应用于人脸检测 • WLD深入探讨 • 结论

  9. 讲解提纲 • 相关知识介绍 • 用于图像描述的WLD算子 • WLD应用于纹理分类 • WLD应用于人脸检测 • WLD深入探讨 • 结论

  10. Descriptor • Sparse Descriptor • SIFT • PCA-SIFT • HOG • RIFT • Dense Descriptor • LBP • Gabor wavelet • Cross Descriptor • Use SIFT in dense way • Use LBP in sparse way

  11. SIFT • Scale Invariant Feature Transform，尺度不变特征变换匹配算法，对旋转、尺度缩放、亮度变化等保持不变性。 • 尺度空间极值检测。在尺度空间通过高斯微分函数来检测潜在的对于尺度和旋转不变的兴趣点。 • 关键点定位。在兴趣点位置上，确定关键点的位置和尺度。 • 方向确定。基于图像局部的梯度方向，给每个关键点分配方向。 • 关键点描述。在每个关键点的领域内测量图像局部的梯度。最终用一个特征向量来表达。

  12. LBP

  13. Weber’s Law • 韦伯定律是表明心理量和物理量之间关系的定律。德国生理学家和心理学家韦伯(1795－1878)发现同一刺激差别量必须达到一定比例，才能引起差别感觉。这一比例是个常数，用公式表示： ΔI/I = k • ΔI：差别阈限 • I：标准刺激强度 • k：常数/韦伯分数

  14. 讲解提纲 • 相关知识介绍 • 用于图像描述的WLD算子 • WLD应用于纹理分类 • WLD应用于人脸检测 • WLD深入探讨 • 结论

  15. WLD • Weber Local Descriptor,is a dense descriptor • Employs the advantages of SIFT and LBP • SIFT: compute the histogram using the gradient and its orientation • LBP: computational efficiency and smaller support regions • Two components of WLD • Differential excitation (差分激励) • Orientation (方向)

  16. WLD计算过程 差分激励 梯度方向

  17. Differential Excitation(ξ )

  18. Differential Excitation(ξ)

  19. Using arctangent function

  20. Orientation(θ)

  21. Orientation(θ) T=8

  22. WLD Histogram • j=1,2,…N-1, N is the dimensionality of an image • t=1,2,…T-1, T is the number of the dominant orientations • The size of this 2D histogram is T*C, where C is the number of cells in each orientation, and C=M*S.

  23. WLD Histogram WLD=T*C T=8 M=6, S=3 C=M*S=18

  24. Weight for WLD Histogram

  25. Characteristics of WLD • WLD is based on a physiological law, it has powerful representation ability for textures. • The detected edges match the subjective criterion elegantly since WLD depends on the perceived luminance difference. • WLD is robust to noise appearing in a given image. • WLD is robust to changes in illumination.

  26. Characteristics of WLD

  27. MultiscaleAnalysis • WLDP, R • PSizeLength = 2*R+1 • One can also generalize them to a circular one.

  28. Comparison

  29. Comparison

  30. 讲解提纲 • 相关知识介绍 • 用于图像描述的WLD算子 • WLD应用于纹理分类 • WLD应用于人脸检测 • WLD深入探讨 • 结论

  31. Texture Classification • Brodatz • KTH-TIPS2-a

  32. Texture Classification • Set M = 6, T = 8, S = 20 • Weight each subhistogramHmusing the weights in the Weight for WLD Histogram table. • Use the K-nearest neighbor as classifier(K=3) • Use the normalized histogram intersection as similarity measurement of two histograms:

  33. Texture Classification

  34. 讲解提纲 • 相关知识介绍 • 用于图像描述的WLD算子 • WLD应用于纹理分类 • WLD应用于人脸检测 • WLD深入探讨 • 结论

  35. Face Detection

  36. Face Detection • Use a P-neighborhood WLD operator (P = 8 and R = 1) • Divide a face sample of size w*h into K overlapping blocks (K = 9) of size (w/2)*(h/2) pixels. • Set M = 6, T = 4, S = 3 • Train a SVM classifier

  37. Face Detection • Training data set • Positive set Sj • Negative set Sn • Test data set • MIT+CMU frontal face test set • Aleix Martinez-Robert(AR) face database • CMU profile testing set

  38. Face Detection

  39. Face Detection

  40. Face Detection • MIT+CMU frontal face test set • Aleix Martinez-Robert(AR) face database • CMU profile testing set

  41. 讲解提纲 • 相关知识介绍 • 用于图像描述的WLD算子 • WLD应用于纹理分类 • WLD应用于人脸检测 • WLD深入探讨 • 结论

  42. WLDarctan V.S. WLDlog

  43. The Effect of Parameters

  44. Different Filters

  45. Components

  46. Robustness to Noise

  47. 讲解提纲 • 相关知识介绍 • 用于图像描述的WLD算子 • WLD应用于纹理分类 • WLD应用于人脸检测 • WLD深入探讨 • 结论

  48. Conclusion • This paper propose a novel discriminative descriptor called WLD. It is inspired by Weber’s Law, and it consists of two components: differential excitations and orientations. • Experimental results show that WLD illustrates a favorable performance on both texture classification and face detection. • Future interest lies in how to exploit the proposed descriptor for the domain of face recognition and object recognition

  49. Thank you!

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