MACHINE LEARNING CLASSIFIERS ADVANTAGES AND CHALLENGES OF SELECTED METHODS

MACHINE LEARNING CLASSIFIERS ADVANTAGES AND CHALLENGES OF SELECTED METHODS FRANK ORBEN, TECHNICAL SUPPORT / DEVELOPER IMAGE PROCESSING, STEMMER IMAGING

OUTLINE Introduction Task: Classification Theory Descriptors & feature space MRF/Ridge Regression (CVB Polimago) Conv. Neural Networks (TensorFlow ) Application Advantages & challenges Use cases Classification performance Excursion: Object search Summary SLIDE 2

TASK: CLASSIFICATION Classification assigning a class label to an object Objects image or ROI Features Shape Size Structure Gradient Edges Feature descriptor? Descriptor Classifier Classification SLIDE 3

DESCRIPTORS IN FEATURE SPACE Descriptor parametrical description of an object Feature space mapping of objects based on their descriptors 4 3,5 3 2,5 2 1,5 1 0,5 0 0 0,5 1 1,5 2 Descriptors Feature Space SLIDE 4

FEATURE SPACE OVER & UNDER FITTING Underfitting model is not accurate enough Low consistency High generalization Overfitting model is too accurate High consistency Weak generalization [WikiCommons1] Goal: compromise [WikiCommons2] SLIDE 5

TRAINING & TRAINING SET Training creating classifiers based on a training set A B C Classification Algorithm SVM KNN Regression ANN Classifier Training Set Descriptor Training SLIDE 6

MULTI RESOLUTION FILTER MRF feature extraction Multi scale analysis Convolution on different scale levels Various filters Abstract description Structure Gradients Local intensities Feature vector for every image of a class [WikiCommons3] SLIDE 7

REGULARIZATION& RIDGE REGRESSION Regression analysis Models relationships between variables Model description Regularization Solves ill-posed problems Adds additional information [WikiCommons4] Tikhonov Regularization (Andrey Tikhonov, 1960s) Regularization with weighted matrix Ridge Regression (special case of Tikhonov Reg.) Identity matrix [WikiCommons5] SLIDE 8

REGULARIZATION& RIDGE REGRESSION Training Descriptors > mapped as high-dimensional equation system Classifier calculation, Regularization with Ridge Regression Classifier Regressions predictor for every class pair Classifier: all regression predictors of a training set Classification Convolution of feature descriptor with classifier Scalar for every class pair Assigning class labels SLIDE 9

ARTIFICIAL NEURAL NETWORK - DEEP LEARNING Artificial neural network(1980s) Viable thanks to wide spread of GPGPU Imitation of biological nerve system Neuron/Unit multiple inputs > single output Net of neurons/units [WikiCommons7] Back propagating Hidden Layer Output Layer Output Layer [WikiCommons7/8] SLIDE 10

CNN CONVOLUTION LAYER Convolution Pooling Randomly initialized filters [WikiCommons9] Abstract description (structure, intensity, ) Feature extraction Self-optimizing (back propagating) SLIDE 11

CNN PUTTING IT ALL TOGETHER CNN = convolution stage + fully connected stage Training: repeated iterations for whole training set Result: optimized net after N iterations (epoch) Classification: image passes the net once Assigning class labels? convolution, pooling fully connected SLIDE 12

ADVANTAGES RR/MRF & CNN No pre-processing necessary (image size consistent for training set) Robust features for many use cases Low requirements regarding knowledge of feature selection CNN Self-optimization of feature extraction and classification stage Variable specialization via depth of net Robust for similar objects (suitable parameterization) RR/MRF Variable specialization via MRF & regularization parameters Robust with small training sets SLIDE 13

CHALLENGES General Higher number of classes > lengthy processing (training & classification) Image size is fixed per training set CNN (current parameterization/implementation) Overfitting (counter measures exist) Comprehensively large sets Dedicates hardware (GPU) for reasonable training times RR/MRF (current parameterization/implementation) Maximum number of classes is restricted Negligible acceleration through GPU SLIDE 14

USE CASES Identify/distinguish objects Quality control in manufacturing Distinguish good from bad parts Statistical analysis Counting people Counting vehicles Automated data processing Text recognition Batch processing Sorting SLIDE 15

CLASSIFICATION PERFORMANCE Vehicle Wafer OCR MNIST 240x240 px 8bit RGB 1,7k images 6 classes 63x68 px 8bit mono 14k images 10 classes 28x28 px binary 60k images 10 classes RR VEHICLE RR WAFER RR MNIST CNN VEHICLE CNN WAFER CNN MNIST Top-1-Error 5.18 2.02 1.89 5.05 5.75 1.95 Train. Time 0.12 min 8 min 14 h 7 min 23 min 20 min 62 min Class. Time 0.048 ms 0.045 ms 0.007 ms 1.722 ms 1.288 ms 1.791 ms SLIDE 16

EXCURSION: OBJECT SEARCH target Object search > reg. pred. for every degree of freedom (example, translation only) SLIDE 17

SUMMARY Traditional approach ridge regression (with multi resolution filter) Viable thanks to current technology CNN Many application areas Large number of classes > longer training times Hardware requirements Size of training set > constrains depend on use case SLIDE 18

THANK YOU FOR YOUR ATTENTION Your contact Frank Orben STEMMER IMAGING GmbH +49 89 80902-747 f.orben@stemmer-imaging.de www.stemmer-imaging.de Copyright STEMMER IMAGING. All texts, images, graphs, tone, video and animation files as well as their arrangements are subject to copyright law and other laws for the protection of intellectual property. They may not be copied or changed for any commercial use or for the purpose of being passed on nor used on other websites. Some of the pages of the STEMMER IMAGING presentation also contain images that are subject to the copyright belonging to those persons who have made them available

BIBLIOGRAPHY [WikiCommons1] https://commons.wikimedia.org/wiki/file:traintest.svg Web, Oct. 11th 2017 [WikiCommons2] https://commons.wikimedia.org/wiki/file:overfitting.svg Web, Oct. 11th 2017 [WikiCommons3] https://commons.wikimedia.org/wiki/file:image_pyramid.svg Web, Oct. 11th 2017 [WikiCommons4] https://commons.wikimedia.org/wiki/file:normdist_regression.png Web, Oct. 11th 2017 [WikiCommons5] https://commons.wikimedia.org/wiki/file:regularization.svg Web, Oct. 11th 2017 [WikiCommons6] https://commons.wikimedia.org/wiki/file:artificialneuronmodel_english.png Web, Oct. 11th 2017 [WikiCommons7] https://commons.wikimedia.org/wiki/file:single-layer_neural_network-vector-blank.svg Web, Oct. 11th 2017 [WikiCommons8] https://commons.wikimedia.org/wiki/file:multi-layer_neural_network-vector-blank.svg Web, Oct. 11th 2017 [WikiCommons9] https://commons.wikimedia.org/wiki/file:typical_cnn.png Web, Oct. 11th 2017 [LeCun] http://www.iro.umontreal.ca/~bengioy/talks/dl-tutorial-nips2015.pdf, Web, Oct. 11th 201 MACHINE VISION CLASSIFIERS ADVANTAGES AND CHALLENGES OF SELECTED METHODS SLIDE 20

APPENDIX MACHINE VISION CLASSIFIERS ADVANTAGES AND CHALLENGES OF SELECTED METHODS SLIDE 21

APPENDIX CNN FEATURE MAPS.[LeCun] MACHINE VISION CLASSIFIERS ADVANTAGES AND CHALLENGES OF SELECTED METHODS SLIDE 22

APPENDIX - TIKHONOV REGULARIZATION Know matrix and vector, looking for vector such that: Approach: ordinary least squares, minimize To give preference to a particular solution, introduce regularization term Tikhonov matrix Regularization improves conditioning, enabling a direct numerical solution MACHINE VISION CLASSIFIERS ADVANTAGES AND CHALLENGES OF SELECTED METHODS SLIDE 23