IEEE ICIP Feature Normalization for Part-Based Image Classification

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Presentation transcript:

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 http://www.tsinghua.edu.cn

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 2013 - Oral Presentation

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 2013 - Oral Presentation

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

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 2013 - Oral Presentation

Spatial Pyramid Matching (SPM) = Part 1 [Lazebnik, CVPR06] = Part 2 = Part 3 = Part 4 = Part 5 11/18/2018 ICIP 2013 - Oral Presentation

Hierarchical Part Matching (HPM) = Part 1 [Xie, ICCV13] = Part 2 = Part 3 = Part 4 = Part 5 11/18/2018 ICIP 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

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

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

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 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

Separate Normalization Normalizing each Part Individually part-wise vector coefficient 11/18/2018 ICIP 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

Hierarchical Normalization Assigning more Weights on High-Level Parts hierarchical contribution part-wise weight part-wise coefficient 11/18/2018 ICIP 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

ICIP 2013 - Oral Presentation The CUB-200-2011 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 2013 - Oral Presentation

ICIP 2013 - Oral Presentation The CUB-200-2011 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 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

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 2013 - Oral Presentation

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