Learning Methods for Generic Object Recognition with Invariance to Pose and Lightingby Yann LeCun, Fu Jie Huang, and Léon Bottouin Proceedings of CVPR'04, 2004

Presentation by Hasan Doğu TAŞKIRANCS 550 – Machine Learning

Department of Computer EngineeringBilkent University

April 21, 2005

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About the paper… Recognition of Generic Object Categories The NORB Dataset Experiments and Results

Principal Component Analysis K-Nearest Neighbors Pairwise Support Vector Machines Convolutional Networks

Conclusion and Future Work

Outline

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The paper is about…

Describing the largest publicly available dataset

Reporting baseline performance with standard methods on this dataset

Exploring how methods fare when the number of input variables is huge

The performance of methods based on global template matching

The performance when the size of the problem is at the upper-limit of applicability

Learning invariance to 3D pose, lighting conditions and variabilities of images

Taking advantage of binocular inputs

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Recognition of Generic Object Categories

The recognition of generic object categories with invariance to pose, lighting, diverse backgrounds, and the presence of clutter is one of the major challenges of Computer Vision.

Variety of clues have been used previously: Color and Texture Distinctive Local Features Separately acquired 3D models Silhouettes and edges Pose-invariant Feature Histograms

Shape information??

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Using Shape Information

Recognizing Generic Categories such as cars, trucks, airplanes, human figures, or four-legged animals purely from the shape information is a difficult problem

Another difficulty of the problem is the non-availability of a dataset with sufficient size and diversity to carry out meaningful experiments.

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The NORB Dataset

The only useful and reliable clue in the dataset is the shape of the object

NORB is considerably larger than the past datasets and it offers: More variability Stereo pairs The ability to composite the objects and their cast shadows onto

diverse backgrounds

Images of 50 toys were collected using the peripherals whose details are given in the paper

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The NORB Dataset

The collection consists of 10 instance of 5 generic categories: Four-legged Animals, Human Figures, Airplanes, Trucks, Cars All objects are painted uniform green to eliminate irrelevant color and texture Each object instance was placed in a different initial pose 1944 stereo pairs were collected for each instance: 9 elevations, 36 azimuths

and 6 lighting conditions A total of 194.400 images RGB images of resolution 640x480 were collected (5

categories, 10 instances, 9 elevations, 36 azimuths, 6 lightings, and 2 cameras)

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The NORB Dataset

Experiments were conducted with 4 datasets generated from the normalized object images

Normalized-Uniform Set Jittered-Uniform Set Jittered-Textured Set Jittered-Cluttered Set

Each dataset consists of the 5 instances of categories for training and 5 instances for testing

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The NORB Dataset

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On raw image pairs Linear Classifier K-Nearest Neighbor Pairwise Support Vector Machines with Gaussian Kernels Convolutional Networks

On PCA coefficients K-Nearest Neighbor Pairwise Support Vector Machines with Gaussian Kernels

Lush environment, Torch Library are used

Experiments

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18,432 x 18,432 covariance matrix so we need a method

Find the principal direction of a centered cloud of points by finding two cluster centroids that are symmetric with respect to the origin i.e., find u that minimizes

Yields the first 100 principal components in a few CPU hours

Experiments - PCA

i ii uxux ))(,)min(( 22

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Running on 24,300 reference images of size 18,432 is prohibitively expensive

Pre-compute the distances of a few representative images Ak to all other reference images Xi.

Distances are bounded below by:

This can be used to choose which distances should be computed first.

Conducted up to K = 18 but best results are obtained for K = 1

Experiments – K-Nearest Neighbors

),(),( ikkk XAdAXdMax

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Failed to obtain convergence on normalized-uniform dataset in manageable time, also SVMs were not trained on jitter datasets

Applied on sub-sampled versions and PCA-derived versions

10 SVMs were independently trained to do pairwise classification and used voting strategy

The number of support vectors was between [800, 2000] for PCA-derived inputs

The number of support vectors was between [2000, 3000] for 32x32 raw images

Experiments – Pairwise SVM

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Succession of layers of trainable convolutions and spatial sub-sampling

Extracts features of: Increasingly large receptive fields Increasing complexity Increasing robustness to irrelevant

variabilities

The network has 90,575 trainable parameters (Full propagation requires 3,896,920 multiply-adds)

Levenberg-Marquardt algorithm with diagonal approximation of the Hessian for 250,000 online updates

No over-training, no early-stops.

Experiments – Convolutional Network

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Results

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Results

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Results

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Discussion

These are the first systematic experiments that apply machine learning to shape-based generic object recognition with invariance to pose and lighting

Normalized-uniform dataset is unrealistically favorable to template-based methods because of the perfect conditions

The size of the jittered database was too large to carry out experiments with the template based methods

The shear size and complexity of the jittered datasets place them above the practical limits of template based methods.

Binocular convolution network take advantage of disparity information to locate the outline of the object

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Conclusions

The system can spot and recognize the animals, human figures, planes, cars and trucks in natural scenes with high accuracy at a rate of several frames per second

By presenting the input image at multiple scales, the system can detect those objects over a wide range of scales

Popular template-based approaches including SVMs are limited for classification over very large datasets with complex variabilities.

Convolutional Networks can be scanned over large images very efficiently

The NORB Dataset opens the door to large-scale experiments with learning-based approaches to invariant object recognition

Future works may use trainable classifiers that incorporate explicit models of image formation and geometry

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Comments

The authors just dealt with their problems, not to the specific problems of the algorithms

The paper is well organized and clearly understandable

The dataset preparation details might be reduced

Previous works in the area could be discussed more with their disadvantages

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Questions?