1. LDP Local Directional Pattern & LDN Local Directional Number Pattern
报告人：黄倩颖

2. 内容 两种局部编码模式构造描述子 LDP Local Directional Pattern
LDN Local Directional Number Pattern
对Local Binary Pattern (LBP)的改良

3. Descriptor geometric-feature-based appearance-based
geometric-feature-based –sparse 稀疏
appearance-based methods -dense 密集
geometric-feature-based
appearance-based

4. Part One
作者简介
文章结构
方法概述
讲解提纲
LBP方法回顾
LDP的创新点
LDP的鲁棒性
LDP的旋转不变性 实验 结论

5. 作者简介
Local Directional Pattern (LDP) – A Robust Image Descriptor for Object Recognition
Taskeed Jabid, Md. Hasanul Kabir, Oksam Chae
Department of Computer Engineering Kyung Hee University, Republic of Korea
2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance
Taskeed Jabid
Human Computer Interaction, Computer Vision, Object Recognition
Local Directional Pattern (LDP) for face recognition
International Conference Consumer Electronics (ICCE), 2010
Cited by 44
同一个内容的论文投了很多会议

6. 文章结构 Introduction LDP image descriptor
Local Binary Pattern (LBP)
Local Directional Pattern (LDP)
Robustness of LDP
Rotation invariant LDP
LDP Descriptor
Texture classification using LDP descriptor
Face recognition using LDP descriptor
Conclusions

7. Abstract LDP( Local Directional Pattern) is
a local feature descriptor for describing local image feature.
Though LBP is robust to monotonic illumination change but it is sensitive to non-monotonic illumination variation and also shows poor performance in the presence of random noise
A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy situation.
非线性光 随机噪点
八个方向的边缘响应值
相对强度大小
考虑了周边的值，因此更具有鲁棒性

8. Part One
作者简介
文章结构
方法概述
讲解提纲
LBP方法回顾
LDP的创新点
LDP的鲁棒性
LDP的旋转不变性 实验 结论

9. 讲解提纲
LBP方法回顾
LDP的创新点
LDP的鲁棒性
LDP的旋转不变性 实验 结论

10. Local Binary Pattern (LBP)
Original LBP
26 < 85 32 26 53 50 10 60 38 45 1
Threshold 50
前情提要
选定一个位置，一个方向开始编码
（ ）2 = 56

11. Local Directional Pattern (LDP)
Kirsch masks
North 5 -3 5 -3 -3 5 M3 M2 M1
North-West
North- East 5 -3 M3 M2 M1 M4 M0 M5 M6 M7
399 M0 -3 5 85 32 26 53 50 10 60 38 45 M4
West
East
Keshk
加权求和 求各个方向趋势 -3 5 -3 5 -3 5 M5 M6 M7
South-
West
South-
East
South

12. 19 Computing… LDPk Kirsch masks 85 32 26 53 50 10 60 38 45 k=3
313 97
503
537
399
161
Kirsch masks 19
LDPk
k=3 1
K默认取3
从东边开始绕中心一周
LDP Binary Code =
LDP Decimal Code= 19

13. Robustness of LDP noise & non-monotonic illumination changes -4 -3 -685 32 26 53 50 10 60 38 45 81 29 32 38 58 15 65 43 47 -4 -3 -6
-15 +8 +5 +2 85 32 26 53 50 10 60 38 45
重点：A Robust Image Descriptor
Since edge responses are more stable than intensity values,
LDP pattern provides the same pattern value even presence of noise and non-monotonic illumination changes.
优于LBP
LBP =
LDP =
LBP =
LDP =

14. Rotation invariant LDP85 32 26 53 50 10 60 38 45 1
313 97
503
537
399
161 26 10 45 32 50 38 85 53 60 1
503
393
161 97
313
537
旋转不变性
总是从1开始 减少了描述子的个数
Rotation Invariant LDP Code =

15. LDP Descriptor
Accumulating the occurrence of LDP feature
统计直方图

16. Experiments Texture Classification using LDP histogram
Primary pictures from Brodatz texture album:
(a) Bark,
(b) Brick,
(c) Bubbles,
(d) Grass,
(e) Leather,
(f) Pigskin,
(g) Raffia,
(h) Sand,
(i) Straw,
(j) Water,
(k) Weave,
(l) Wood and
(m) Wool

17. Experiments
Texture Classification using LDP histogram

18. Experiments
Extracted rotation invariant LDP features of each pixel of the image then combined to generate rotation invariant image descriptor using LDP histogram following equation.

19. Experiment Results
The accuracy of the method
Results

20. Face recognition using LDP descriptor
Database FERET
(a) fa set, used as a gallery set, contains frontal images of 1,196 people.
(b) fb set (1,195 images) with an alternative facial expression than in the fa photograph.
(c) fc set (194 images) taken under different lighting conditions.
(d) dup I set (722 images) taken later in time.
(e) dup II set (234 images) subset of the dup I set containing images that were taken at least a year after the corresponding gallery image.

21. Face recognition using LDP descriptor
Classification using LDP histogram
Template matching

22. Experiment Results

23. Part Two
作者简介
文章结构
方法概述
讲解提纲
LBP LDP缺点
LDN 三个关键点
人脸描述 实验
结论及未来工作

24. 作者简介
Local Directional Number Pattern for Face Analysis: Face and Expression Recognition
Adin Ramirez Rivera,Student Member, IEEE,
Jorge Rojas Castillo,Student Member, IEEE,
and Oksam Chae,Member, IEEE
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 5, MAY 2013
Cited by 2 | Year 2012 |
Adin Ramirez Rivera
Image Processing, Computer Vision
Content-Aware Dark Image Enhancement through Channel Division
IEEE Transactions on Image Processing 21 (9),
Cited by 9 | Year 2012
较新的论文

25. 文章结构 Introduction Local Directional Number Pattern Face description
Difference With Previous Work
Coding Scheme
Compass Masks
Face description
Face recognition
Conclusions

26. A novel local feature descriptor
Abstract
A novel local feature descriptor
LDN encodes the directional information of the face’s textures in a compact way, producing a more discriminative code than current methods
具有识别性

27. Part Two
作者简介
文章结构
方法概述
讲解提纲
LBP LDP缺点
LDN 三个关键点
人脸描述 实验
结论及未来工作

28. 讲解提纲
LBP LDP缺点
LDN 三个关键点
人脸描述 实验
结论及未来工作

29. LBP The method discards most of the information in the neighborhood.
It limits the accuracy of the method
It makes the method very sensitive to noise
Moreover, these drawbacks are more evident for bigger neighborhoods

30. Directional (LDiP) & Derivative (LDeP)
Miss some directional information
(the responses’ sign) by treating all
directions equally
Sensitive to illumination changes and noise, as the bits in the code will flip and the code will represent a totally different characteristic
边缘响应
方向没有指明 计算的时候忽略了响应的符号问题

31. LDN LBP Key points of LDN Direction number Sign information gradient
The coding scheme is based on directional numbers, instead of bit strings, which encodes the information of the neighborhood in a more efficient way 基于方向码 名字来源
The implicit use of sign information, in comparison with previous directional and derivative methods we encode more information in less space, and, at the same time, discriminate more textures 隐含方向
The use of gradient information makes the method robust against illumination changes and noise
6-bit

32. LDN Key points of LDN Direction number Sign information gradient
逐个来看下他是怎么达到目的的
6-bit

33. Coding Scheme - + - + Direction number Sign information 同一张图像上 方向不同的区分
we pick the prominent information of each pixel’s neighborhood. Therefore,
our method filters and gives more importance to the local
information before coding it, while other methods weight the
grouped (coded) information

34. Coding Scheme

35. Compass Masks 𝐿𝐷𝑁 𝐾 𝐿𝐷𝑁 𝜎 𝐺 Kirsch masks derivative-Gaussian mask
gradient
information
Compass Masks
Two kinds of masks
𝐿𝐷𝑁 𝐾
Kirsch masks
𝐿𝐷𝑁 𝜎 𝐺
derivative-Gaussian mask

36. M3 M2 M1 M4 M0 M5 M6 M7 Compass Masks Kirsch masks North 5 -3 5 -3 -35 -3 -3 5 M3 M2 M1
North-West
North- East 5 -3 M3 M2 M1 M4 M0 M5 M6 M7 M4 M0 -3 5
West
East -3 5 -3 5 -3 5 M5 M6 M7
South-
West
South-
East
South

37. Compass Masks derivative-Gaussian mask Compute code in gradient space
Therefore, use Gaussian smoothing to stabilize the code in presence of noise
受 Kirsch Mask的启发
Generate a compass mask,{M0σ,...,M7σ}, by rotating Mσ, 45°apart, in eight different directions

38. Compass Masks
derivative-Gaussian mask

39. Face Descriptor
Histogram
LH & MLH

40. Face Descriptor
Two kinds of descriptor
Code in LH
Code in MLH must be

41. Face Recognition
Chi-Square dissimilarity measure

42. Face recognition using LDP descriptor
Database FERET
(a) fa set, used as a gallery set, contains frontal images of 1,196 people.
(b) fb set (1,195 images) with an alternative facial expression than in the fa photograph.
(c) fc set (194 images) taken under different lighting conditions.
(d) dup I set (722 images) taken later in time.
(e) dup II set (234 images) subset of the dup I set containing images that were taken at least a year after the corresponding gallery image.

43. Experiment Results Face recognition accuracy
small neighborhoods (3×3, 5×5, 7×7)
medium neighborhoods (5×5, 7×7, 9×9)
large neighborhoods (7×7, 9×9, 11×11)
没有预处理
LPQ GGPP 相位信息 phase information

44. Experiment Results Noise Evaluation With white Gaussian noise
GGPP Global Gabor Phase Pattern

45. Conclusion
Combination of different sizes (small, medium and large) gives better recognition rates for certain conditions.
Evaluated LDN under expression, time lapse and illumination variations, and found that it is reliable and robust throughout all these conditions.
在特定情况下，使用不同大小的组合达到更好效果
经过作者的测试，LDN能经受表情、时间变化、光照变化等考验，在各种方法中表现较好

46. 总结及未来工作 如何选择一个描述子 如何设计一个描述子 长度 描述精度 抗噪能力 计算强度 舍弃冗余的信息 整合多种信息来源 信息压缩
作为一个预科研究生，困惑这个总结应该怎么讲。
那我只要就从这个描述子说起
一个算法拿来做什么达到什么样的效果。
写论文就是写小说。武侠风格尤佳。首先营造一个紧张的氛围，烘托出一个必须解决的严重问题。接着绿叶登场，挨个败下阵来。然后主人公出现，在精巧的情节布局之下，他的特长刚好得到最大限度地发挥，他的缺陷刚好都不重要，甚至还能变废为宝。于是主人公拯救了学术界，并在剧终谦虚地表示，由于时间有限自己的独门武功还有6层没有修炼完。（李尧）

47. Thank you!