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IEEE ICIP Feature Normalization for Part-Based Image Classification

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Presentation on theme: "IEEE ICIP Feature Normalization for Part-Based Image Classification"— Presentation transcript:

1 IEEE ICIP 2013 Feature Normalization for Part-Based Image Classification
Speaker: Lingxi Xie Authors: Lingxi Xie, Qi Tian, Bo Zhang State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University

2 ICIP 2013 - Oral Presentation
Outline Image Classification The Part-Based Classification Models Feature Normalization Global Normalization Separate Normalization Hierarchical Normalization Experimental Results Conclusions 11/18/2018 ICIP Oral Presentation

3 ICIP 2013 - Oral Presentation
Outline Image Classification The Part-Based Classification Models Feature Normalization Global Normalization Separate Normalization Hierarchical Normalization Experimental Results Conclusions 11/18/2018 ICIP Oral Presentation

4 ICIP 2013 - Oral Presentation
Image Classification A basic task towards image understanding General vs. Fine-Grained 11/18/2018 ICIP Oral Presentation

5 ICIP 2013 - Oral Presentation
Outline Image Classification The Part-Based Classification Models Feature Normalization Global Normalization Separate Normalization Hierarchical Normalization Experimental Results Conclusions 11/18/2018 ICIP Oral Presentation

6 Spatial Pyramid Matching (SPM)
= Part 1 [Lazebnik, CVPR06] = Part 2 = Part 3 = Part 4 = Part 5 11/18/2018 ICIP Oral Presentation

7 Hierarchical Part Matching (HPM)
= Part 1 [Xie, ICCV13] = Part 2 = Part 3 = Part 4 = Part 5 11/18/2018 ICIP Oral Presentation

8 ICIP 2013 - Oral Presentation
Outline Image Classification The Part-Based Classification Models Feature Normalization Global Normalization Separate Normalization Hierarchical Normalization Experimental Results Conclusions 11/18/2018 ICIP Oral Presentation

9 Feature Normalization
A Key Step before Classifiers Data pre-processing. Equivalent the range and weight of input vectors. Significantly impact the classification results. Different Models, Different Normalization. Support Vector Machine (SVM) Naïve Bayes Classificer (NB) Hidden Markov Models (HMM) 11/18/2018 ICIP Oral Presentation

10 ICIP 2013 - Oral Presentation
Global Normalization Considering the Feature as a Whole. coefficient norm 11/18/2018 ICIP Oral Presentation

11 ICIP 2013 - Oral Presentation
Global Normalization head head body body black-footed albatross sooty albatross 11/18/2018 ICIP Oral Presentation

12 ICIP 2013 - Oral Presentation
Global Normalization 1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL not balanced 11/18/2018 ICIP Oral Presentation

13 FAIR? Global Normalization Small Parts have Low Feature Weights. 1 2 3
4 5 6 head body ALL 1 2 3 4 5 6 head body ALL FAIR? 11/18/2018 ICIP Oral Presentation

14 beak wing Global Normalization
However, Small Parts are also Important. groove billed ani common raven red winged blackbird rusty blackbird beak wing 11/18/2018 ICIP Oral Presentation

15 Separate Normalization
Normalizing each Part Individually part-wise vector coefficient 11/18/2018 ICIP Oral Presentation

16 Separate Normalization
1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL Small Parts are Given Equal Weights! 11/18/2018 ICIP Oral Presentation

17 Separate Normalization
Different-Level Parts have Same Weight. 1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL FAIR? 11/18/2018 ICIP Oral Presentation

18 Separate Normalization
Examples of Hierarchical Parts in Birds Dataset. head = beak + eyes + crown + forehead neck = nape + throat body = breast + back + belly + wings ALL = head + body + tail + legs Some Observations: High-level parts contain more information. High-level parts are less likely to be missing. 11/18/2018 ICIP Oral Presentation

19 Hierarchical Normalization
Assigning more Weights on High-Level Parts hierarchical contribution part-wise weight part-wise coefficient 11/18/2018 ICIP Oral Presentation

20 Hierarchical Normalization
1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL 1 2 3 4 5 6 head body ALL High-Level Parts are Enhanced! 11/18/2018 ICIP Oral Presentation

21 ICIP 2013 - Oral Presentation
Outline Image Classification The Part-Based Classification Models Feature Normalization Global Normalization Separate Normalization Hierarchical Normalization Experimental Results Conclusions 11/18/2018 ICIP Oral Presentation

22 ICIP 2013 - Oral Presentation
The Caltech101 Dataset General Object Recognition [Fei-Fei, CVIU07] 102 classes (one background category) 9144 images Models SPM [Lazebnik, CVPR06] + LLC [Wang, CVPR10] SPM [Lazebnik, CVPR06] + GPP [Xie, MM12] SPM [Lazebnik, CVPR06] + EdgeGPP [Xie, MM12] 16 Basic Parts + 5 High-Level Parts 11/18/2018 ICIP Oral Presentation

23 ICIP 2013 - Oral Presentation
The Caltech101 Dataset SPM+LLC SPM+GPP SPM+EdgeGPP No Normalization 73.14 76.35 80.78 Global L1-norm 73.91 76.26 80.86 Global L2-norm 74.41 77.03 82.45 Global Li-norm 73.25 76.47 80.89 Separate L1-norm 71.99 75.20 78.05 Separate L2-norm 73.68 75.47 81.24 Separate Li-norm 73.39 76.43 80.93 Hierarchical L1-norm 72.71 75.88 80.40 Hierarchical L2-norm 74.31 76.86 83.19 Hierarchical Li-norm 73.89 76.55 81.37 11/18/2018 ICIP Oral Presentation

24 ICIP 2013 - Oral Presentation
The CUB Dataset Fine-Grained Bird Classification [Wah, TR11] 200 species of birds 11788 images Models Parts [Xie, ICCV13] + LLC [Wang, CVPR10] HPM [Xie, ICCV13] + LLC [Wang, CVPR10] HPM [Xie, ICCV13] + GPP [Xie, MM12] 15 Basic Parts + 6 High-Level Parts 11/18/2018 ICIP Oral Presentation

25 ICIP 2013 - Oral Presentation
The CUB Dataset Parts+LLC HPM+LLC HPM+GPP No Normalization 25.58 27.55 30.67 Global L1-norm 27.61 29.85 33.22 Global L2-norm 27.06 29.30 32.96 Global Li-norm 25.04 27.41 30.71 Separate L1-norm 24.58 26.94 31.84 Separate L2-norm 30.93 32.75 35.98 Separate Li-norm 27.73 29.67 31.92 Hierarchical L1-norm - 28.12 33.85 Hierarchical L2-norm - 32.89 36.48 Hierarchical Li-norm - 29.45 32.08 11/18/2018 ICIP Oral Presentation

26 ICIP 2013 - Oral Presentation
Discussions The Performance of Our Model SPM: only comparable with the original model. HPM: significantly better! Why? Both Assumptions Sound? There exist more semantics in HPM! 11/18/2018 ICIP Oral Presentation

27 ICIP 2013 - Oral Presentation
Outline Image Classification The Part-Based Classification Models Feature Normalization Global Normalization Separate Normalization Hierarchical Normalization Experimental Results Conclusions 11/18/2018 ICIP Oral Presentation

28 ICIP 2013 - Oral Presentation
Conclusions Feature Normalization An important issue in image representation. In Part-Based Classification Models Instructive to consider each part separately. 3 Normalization Strategies Global Normalization Separate Normalization Hierarchical Normalization Easy to Implement! 11/18/2018 ICIP Oral Presentation

29 ICIP 2013 - Oral Presentation
Thank you! Questions please? 11/18/2018 ICIP Oral Presentation

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