Object Recognition

So what does object recognition involve?

Verification: is that a bus?

Detection: are there cars?

Identification: is that a picture of Mao?

Object categorization

sky

building

flag

wallbanner

bus

cars

bus

face

street lamp

Challenges 1: view point variation

Michelangelo 1475-1564

Challenges 2: illumination

slide credit: S. Ullman

Challenges 3: occlusion

Magritte, 1957

Challenges 4: scale

Challenges 5: deformation

Xu, Beihong 1943

Challenges 7: intra-class variation

Two main approaches

Part-basedGlobal sub-window

Global Approaches

x1 x2 x3

Vectors in high-dimensional space

Aligned images

x1 x2 x3

Vectors in high-dimensional space

Global Approaches

Training

Involves some dimensionality

reduction

Detector

– Scale / position range to search over

Detection

Detection– Scale / position range to search over

Detection– Scale / position range to search over

Detection– Combine detection over space and scale.

PROJECT 1

Build a detection system that inputs an image, runs a detector over (x,y) and scales, and removes spurious detections. The system should be able to run different detectors. For initial testing use linear SVM (existing package).

Challenge:• Algorithm for integration of raw detections. • Speed.

• Turk and Pentland, 1991• Belhumeur et al. 1997• Schneiderman et al. 2004• Viola and Jones, 2000• Keren et al. 2001• Osadchy et al. 2004

• Amit and Geman, 1999• LeCun et al. 1998• Belongie and Malik, 2002

• Schneiderman et al. 2004• Argawal and Roth, 2002• Poggio et al. 1993

Antiface method for detection

• No training on negative examples is required.• A set of rejectors is applied in cascaded manner.

• Robust to large pose variation.• Simple and very fast.

Intuition

Lower probability

Lower probability

dxdyII yxeIP 22

)(

image smoothness measure

Boltzmann distribution

How are the natural images distributed in a high dimensional space?

Lower probability

Lower probability

Antiface Much less false positives

PCA Many false positives

Intuition

Main Idea Claim: for random natural images viewed as

unit vectors,

yx, y x,

is large on average.is large on average.

– for all positive classxd , x

– d is smooth

xd , is large on average for random natural image.

Anti-Face detector is defined as a vector d satisfying:

Discrimination

x

xxd ,

xd ,

x

SMALL

LARGE

If x is an image and is a target class:

Cascade of Independent Detectors

1d

2d

3d

7 inner products

4 inner products

Example

Samples from the training set

4 Anti-Face Detectors

4 Anti-face Detectors4 Anti-face Detectors

Eigenface method with the subspace of dimension 100

PROJECT 2

• Implement Antiface method for detection*.• Implement several extensions of Antifaces:

– Change the accepting rule so that instead of passing all the detectors it passes at least 80% of detectors.

– Apply Naïve Bayes in 10D antiface space – Project each image onto 20D Antiface space and train

SVM in this space.

See project page for details

* D. Keren M. Osadchy and C. Gotsman, Anti-Faces: A novel, fast method for image detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, No. 7, July 2001, pp. 747-761.

Part-Based Approaches

ObjectObject

Bag of ‘words’Bag of ‘words’

Constellation of partsConstellation of parts

Of all the sensory impressions proceeding to the brain, the visual experiences are the dominant ones. Our perception of the world around us is based essentially on the messages that reach the brain from our eyes. For a long time it was thought that the retinal image was transmitted point by point to visual centers in the brain; the cerebral cortex was a movie screen, so to speak, upon which the image in the eye was projected. Through the discoveries of Hubel and Wiesel we now know that behind the origin of the visual perception in the brain there is a considerably more complicated course of events. By following the visual impulses along their path to the various cell layers of the optical cortex, Hubel and Wiesel have been able to demonstrate that the message about the image falling on the retina undergoes a step-wise analysis in a system of nerve cells stored in columns. In this system each cell has its specific function and is responsible for a specific detail in the pattern of the retinal image.

sensory, brain, visual, perception,

retinal, cerebral cortex,eye, cell, optical

nerve, imageHubel, Wiesel

China is forecasting a trade surplus of $90bn (£51bn) to $100bn this year, a threefold increase on 2004's $32bn. The Commerce Ministry said the surplus would be created by a predicted 30% jump in exports to $750bn, compared with a 18% rise in imports to $660bn. The figures are likely to further annoy the US, which has long argued that China's exports are unfairly helped by a deliberately undervalued yuan. Beijing agrees the surplus is too high, but says the yuan is only one factor. Bank of China governor Zhou Xiaochuan said the country also needed to do more to boost domestic demand so more goods stayed within the country. China increased the value of the yuan against the dollar by 2.1% in July and permitted it to trade within a narrow band, but the US wants the yuan to be allowed to trade freely. However, Beijing has made it clear that it will take its time and tread carefully before allowing the yuan to rise further in value.

China, trade, surplus, commerce,

exports, imports, US, yuan, bank, domestic,

foreign, increase, trade, value

Bag of ‘words’ analogy to documents

Interest Point Detectors

• Basic requirements:– Sparse– Informative – Repeatable

• Invariance– Rotation– Scale (Similarity)– Affine

Popular Detectors

Scale Invariant

Affine Invariant

Harris-Laplace Affine

Difference of Gaussians Laplace of Gaussians Scale Saliency (Kadir-Braidy)

Harris-Laplace

Difference of Gaussians

Affine

Laplace of Gaussians

Affine

Affine Saliency (Kadir-Braidy)

The are many others…

See:

1) “Scale and affine invariant interest point detectors” K. Mikolajczyk, C. Schmid,

IJCV, Volume 60, Number 1 - 2004

2) “A comparison of affine region detectors”, K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool, http://www.robots.ox.ac.uk/~vgg/research/affine/det_eval_files/vibes_ijcv2004.pdf

Representation of appearance:Local Descriptors

• Invariance– Rotation– Scale – Affine

• Insensitive to small deformations

• Illumination invariance– Normalize out

SIFT – Scale Invariant Feature Transform

• Descriptor overview:– Determine scale (by maximizing DoG in scale and in space),

local orientation as the dominant gradient direction.Use this scale and orientation to make all further computations invariant to scale and rotation.

– Compute gradient orientation histograms of several small windows (128 values for each point)

– Normalize the descriptor to make it invariant to intensity change

David G. Lowe, "Distinctive image features from scale-invariant keypoints,“ International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.

Feature Detection and Representation

Normalize patch

Detect patches[Mikojaczyk and Schmid ’02]

[Matas et al. ’02]

[Sivic et al. ’03]

Compute SIFT

descriptor

[Lowe’99]

Slide credit: Josef Sivic

…

Feature Detection and Representation

Codewords dictionary formationCodewords dictionary formation

…

Codewords dictionary formationCodewords dictionary formation

Vector quantization

…

Slide credit: Josef Sivic

Codewords dictionary formationCodewords dictionary formation

Fei-Fei et al. 2005

Image patch examples of codewordsImage patch examples of codewords

Sivic et al. 2005

Vector X

Representation

Learning

positive negative

SVM classifier

positive negative

SVM classification

SVM classification

Recognition

SVM(X)

Contains object

Vector X

Representation

Doesn’t contain object

PROJECT 3

• Implement a bag of ‘words’ approach. The method is described in “Visual Categorization with Bags of Keypoints” G.Cruska, C. R. Dance, L.Fan, J.Willamowski,C. Bray.

• Test it on 4 categories (from 101 database): airplanes, faces, cars side, motorbikes, against background.

PROJECT 4

• Implement part based method, described in “Class Recognition Using Discriminative Local Features”, by G. Dorkó, C. Schmid.

• Test it on Oxford object data set.• Compare the performance of the algorithm using

different point detectors. The code for point detectors is provided.

• Compare the performance of the algorithm with original SIFT and with SIFT without rotation invariance. The initial code for SIFT is provided, but should be edited to remove rotation invariance.