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Sparse Coding and Its Extensions for Visual Recognition

Kai Yu

Media Analytics DepartmentNEC Labs America, Cupertino, CA

Visual Recognition is HOT in Computer Vision

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Caltech 101

PASCAL VOC

80 Million Tiny Images

ImageNet

The pipeline of machine visual perception

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Low-level sensing

Pre-processing

Feature extract.

Feature selection

Inference: prediction, recognition

• Most critical for accuracy• Account for most of the computation• Most time-consuming in development cycle• Often hand-craft in practice

Most Efforts in Machine Learning

Computer vision features

SIFT Spin image

HoG RIFT

Slide Credit: Andrew Ng

GLOH

Learning everything from data

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Low-level sensing

Pre-processing

Feature extract.

Feature selection

Inference: prediction, recognition

Machine Learning

Machine Learning

BoW + SPM Kernel

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• Combining multiple features, this method had been the state-of-the-art on Caltech-101, PASCAL, 15 Scene Categories, …

Figure credit: Fei-Fei Li, Svetlana Lazebnik

Bag-of-visual-words representation (BoW) based on vector quantization (VQ)

Spatial pyramid matching (SPM) kernel

Winning Method in PASCAL VOC before 2009

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Multiple Feature Sampling Methods

Multiple Visual Descriptors

VQ Coding, Histogram,

SPM Nonlinear SVM

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Convolution Neural Networks

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• The architectures of some successful methods are not so much different from CNNs

Conv. Filtering Pooling Conv. Filtering Pooling

BoW+SPM: the same architecture

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e.g, SIFT, HOG

VQ Coding Average Pooling (obtain histogram)

Nonlinear SVM

Local Gradients Pooling

Observations: • Nonlinear SVM is not scalable• VQ coding may be too coarse• Average pooling is not optimal• Why not learn the whole thing?

Develop better methods

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Better Coding Better Pooling Scalable Linear

Classifier

Better Coding Better Pooling

Sparse Coding

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Sparse coding (Olshausen & Field,1996). Originally developed to explain early visual processing in the brain (edge detection).

Training: given a set of random patches x, learning a dictionary of bases [Φ1, Φ2, …]

Coding: for data vector x, solve LASSO to find the sparse coefficient vector a

Sparse Coding Example Natural Images Learned bases (1 , …, 64): “Edges”

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0.8 * + 0.3 * + 0.5 *

x 0.8 * 36 + 0.3 * 42

+ 0.5 * 63

[a1, …, a64] = [0, 0, …, 0, 0.8, 0, …, 0, 0.3, 0, …, 0, 0.5, 0] (feature representation)

Test example

Compact & easily interpretableSlide credit: Andrew Ng

Testing:What is this?

Motorcycles Not motorcycles

Unlabeled images

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[Raina, Lee, Battle, Packer & Ng, ICML 07]Self-taught Learning

Testing:What is this?

Slide credit: Andrew Ng

Classification Result on Caltech 101

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64% SIFT VQ + Nonlinear SVM

50%Pixel Sparse Coding + Linear SVM

9K images, 101 classes

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Sparse Coding Max Pooling Scalable Linear

Classifier

Local Gradients Pooling

e.g, SIFT, HOG

Sparse Coding on SIFT [Yang, Yu, Gong & Huang, CVPR09]

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64% SIFT VQ + Nonlinear SVM

73%SIFT Sparse Coding + Linear SVM

Caltech-101

Sparse Coding on SIFT [Yang, Yu, Gong & Huang, CVPR09]

What we have learned?

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Sparse Coding Max Pooling Scalable Linear

Classifier

Local Gradients Pooling

1. Sparse coding is a useful stuff (why?)2. Hierarchical architecture is needed

e.g, SIFT, HOG

MNIST Experiments

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Error: 4.54%

• When SC achieves the best classification accuracy, the learned bases are like digits – each basis has a clear local class association.

Error: 3.75% Error: 2.64%

Distribution of coefficient (SIFT, Caltech101)

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Neighbor bases tend to get nonzero coefficients

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Interpretation 2Geometry of data manifold

• Each basis an “anchor point”• Sparsity is induced by locality: each datum is a linear combination of neighbor anchors.

Interpretation 1Discover subspaces

• Each basis is a “direction”• Sparsity: each datum is a linear combination of only several bases.• Related to topic model

A Function Approximation View to Coding

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• Setting: f(x) is a nonlinear feature extraction function on image patches x

• Coding: nonlinear mapping x a

typically, a is high-dim & sparse

• Nonlinear Learning: f(x) = <w, a>

A coding scheme is good if it helps learning f(x)

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A Function Approximation View to Coding – The General Formulation

Function Approx. Error

≤ An unsupervised learning objective

Local Coordinate Coding (LCC)

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• Dictionary Learning: k-means (or hierarchical k-means)

• Coding for x, to obtain its sparse representation a

Step 1 – ensure locality: find the K nearest bases

Step 2 – ensure low coding error:

Yu, Zhang & Gong, NIPS 09Wang, Yang, Yu, Lv, Huang CVPR 10

Super-Vector Coding (SVC)

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• Dictionary Learning: k-means (or hierarchical k-means)

• Coding for x, to obtain its sparse representation a

Step 1 – find the nearest basis of x, obtain its VQ coding

e.g. [0, 0, 1, 0, …]

Step 2 – form super vector coding:

e.g. [0, 0, 1, 0, …, 0, 0, (x-m3）， 0 ，… ]

Zhou, Yu, Zhang, and Huang, ECCV 10

Zero-order Local tangent

Function Approximation based on LCC

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data points bases

locally linear

Yu, Zhang, Gong, NIPS 10

Function Approximation based on SVC

data pointscluster centers

Piecewise local linear (first-order)Local tangent

Zhou, Yu, Zhang, and Huang, ECCV 10

PASCAL VOC Challenge 2009

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OursBest of

Other Teams DifferenceClasses

No.1 for 18 of 20 categories

We used only HOG feature on gray images

ImageNet Challenge 2010

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~40% VQ + Intersection Kernel

64%~73%Various Coding Methods + Linear SVM

1.4 million images, 1000 classes, top5 hit rate

50%Classification accuracy

Hierarchical sparse coding

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Conv. Filtering Pooling Conv. Filtering Pooling

Learning from unlabeled data

Yu, Lin, & Lafferty, CVPR 11

A two-layer sparse coding formulation

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MNIST Results -- classificationMNIST Results -- classification

HSC vs. CNN: HSC provide even better performance than CNN more amazingly, HSC learns features in unsupervised

manner!31

MNIST results MNIST results -- effect of hierarchical learning -- effect of hierarchical learning

Comparing the Fisher score of HSC and SC

Discriminative power: is significantly improved by HSC although HSC is unsupervised coding 32

MNIST results MNIST results -- learned codebook-- learned codebook

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One dimension in the second layer: invariance to translation, rotation, and deformation

Caltech101 results Caltech101 results -- classification-- classification

Learned descriptor: performs slightly better than SIFT + SC

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Conclusion and Future Work

“function approximation” view to derive novel sparse coding methods.

Locality – one way to achieve sparsity and it’s really useful. But we need deeper understanding of the feature learning methods

Interesting directions– Hierarchical coding – Deep Learning (many papers now!)– Faster methods for sparse coding (e.g. from LeCun’s group)– Learning features from a richer structure of data, e.g., video

(learning invariance to out plane rotation)

References

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• Learning Image Representations from Pixel Level via Hierarchical Sparse Coding, Kai Yu, Yuanqing Lin, John Lafferty. CVPR 2011

• Large-scale Image Classification: Fast Feature Extraction and SVM Training, Yuanqing Lin, Fengjun Lv, Liangliang Cao, Shenghuo Zhu, Ming Yang, Timothee Cour, Thomas Huang, Kai Yu in CVPR 2011

• ECCV 2010 Tutorial, Kai Yu, Andrew Ng (with links to some source codes)

• Deep Coding Networks, Yuanqing Lin, Tong Zhang, Shenghuo Zhu, Kai Yu. In NIPS 2010.

• Image Classification using Super-Vector Coding of Local Image Descriptors, Xi Zhou, Kai Yu, Tong Zhang, and Thomas Huang. In ECCV 2010.

• Efficient Highly Over-Complete Sparse Coding using a Mixture Model, Jianchao Yang, Kai Yu, and Thomas Huang. In ECCV 2010.

• Improved Local Coordinate Coding using Local Tangents, Kai Yu and Tong Zhang. In ICML 2010.

• Supervised translation-invariant sparse coding, Jianchao Yang, Kai Yu, and Thomas Huang, In CVPR 2010

• Learning locality-constrained linear coding for image classification, Jingjun Wang, Jianchao Yang, Kai Yu, Fengjun Lv, Thomas Huang. In CVPR 2010.

• Nonlinear learning using local coordinate coding, Kai Yu, Tong Zhang, and Yihong Gong. In NIPS 2009.

• Linear spatial pyramid matching using sparse coding for image classification, Jianchao Yang, Kai Yu, Yihong Gong, and Thomas Huang. In CVPR 2009.