Tamara Berg Object Recognition – BoF models

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Tamara BergObject Recognition – BoF models

790-133Recognizing People, Objects, & Actions

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Topic Presentations• Hopefully you have met your topic presentations group

members?

• Group 1 – see me to run through slides this week or Monday at the latest (I’m traveling Thurs/Friday). Send me links to 2-3 papers for the class to read.

• Sign up for class google group (790-133). To find the group go to groups.google.com and search for 790-133 (sorted by date). Use this to post/answer questions related to the class.

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ObjectBag of

‘features’

Bag-of-features models

source: Svetlana Lazebnik

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Exchangeability

• De Finetti Theorem of exchangeability (bag of words theorem): the joint probability distribution underlying the data is invariant to permutation.

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Origin 2: Bag-of-words models

US Presidential Speeches Tag Cloudhttp://chir.ag/phernalia/preztags/

• Orderless document representation: frequencies of words from a dictionary Salton & McGill (1983)


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Bag of words for text

· Represent documents as a “bags of words”

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Example

• Doc1 = “the quick brown fox jumped”• Doc2 = “brown quick jumped fox the”

Would a bag of words model represent these two documents differently?

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Bag of words for images

· Represent images as a “bag of features”

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Bag of features: outline1. Extract features


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Bag of features: outline1. Extract features2. Learn “visual vocabulary”


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Bag of features: outline1. Extract features2. Learn “visual vocabulary”3. Represent images by frequencies of

“visual words”


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2. Learning the visual vocabulary

Clustering

…

Slide credit: Josef Sivic

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2. Learning the visual vocabulary

Clustering

…

Slide credit: Josef Sivic

Visual vocabulary

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K-means clustering (reminder)• Want to minimize sum of squared Euclidean

distances between points xi and their nearest cluster centers mk

Algorithm:• Randomly initialize K cluster centers• Iterate until convergence:

• Assign each data point to the nearest center• Recompute each cluster center as the mean of all points assigned

to it

k

ki

ki mxMXDcluster

clusterinpoint

2)(),(


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Example visual vocabulary

Fei-Fei et al. 2005

Image Representation• For a query image

Extract features

Associate each feature with the nearest cluster center (visual word)

Accumulate visual word frequencies over the image

Visual vocabulary

xx

x x

x x

x

x

x

x

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3. Image representation

…..

freq

uenc

y

codewords


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4. Image classification

…..

freq

uenc

y

codewords


Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?

CAR

Image Categorization

Choose from many categoriesWhat is this? helicopter

Image Categorization

Choose from many categoriesWhat is this?

SVM/NBCsurka et al (Caltech 4/7)Nearest NeighborBerg et al (Caltech 101)Kernel + SVMGrauman et al (Caltech 101)Multiple Kernel Learning + SVMsVarma et al (Caltech 101)…

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Visual Categorization with Bags of KeypointsGabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cédric Bray

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Data

• Images in 7 classes: faces, buildings, trees, cars, phones, bikes, books

• Caltech 4 dataset: faces, airplanes, cars (rear and side), motorbikes, background

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Method

Steps:– Detect and describe image patches.– Assign patch descriptors to a set of predetermined

clusters (a visual vocabulary).– Construct a bag of keypoints, which counts the

number of patches assigned to each cluster.– Apply a classifier (SVM or Naïve Bayes), treating the

bag of keypoints as the feature vector– Determine which category or categories to assign to

the image.

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Bag-of-Keypoints Approach

Interesting Point Detection

Key PatchExtraction

FeatureDescriptors Bag of Keypoints Multi-class

Classifier

5.1...

5.01.0

Slide credit: Yun-hsueh Liu

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SIFT Descriptors


Key PatchExtraction


Classifier

5.1...

5.01.0


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Bag of Keypoints (1)

• Construction of a vocabulary– Kmeans clustering find “centroids” (on all the descriptors we find from all the training images) – Define a “vocabulary” as a set of “centroids”, where every centroid represents

a “word”.


Key PatchExtraction


Classifier


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Bag of Keypoints (2)

• Histogram– Counts the number of occurrences of different visual words in each image


Key PatchExtraction


Classifier


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Multi-class Classifier

• In this paper, classification is based on conventional machine learning approaches– Support Vector Machine (SVM)– Naïve Bayes


Key PatchExtraction


Classifier


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SVM

Reminder: Linear SVM

x1

x2 Margin

wT x + b = 0

wT x + b = -1w

T x + b = 1

x+

x+

x-

Support Vectors

Slide credit: Jinwei GuSlide 30 of 113

( ) Tg b x w x

( ) 1Ti iy b w x

21minimize 2w

s.t.

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Nonlinear SVMs: The Kernel Trick With this mapping, our discriminant function becomes:

SV

( ) ( ) ( ) ( )T Ti i

i

g b b

x w x x x

No need to know this mapping explicitly, because we only use the dot product of feature vectors in both the training and test.

A kernel function is defined as a function that corresponds to a dot product of two feature vectors in some expanded feature space:

( , ) ( ) ( )Ti j i jK x x x x

Slide credit: Jinwei Gu

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Nonlinear SVMs: The Kernel Trick

Linear kernel:

2

2( , ) exp( )2i j

i jK

x xx x

( , ) Ti j i jK x x x x

( , ) (1 )T pi j i jK x x x x

0 1( , ) tanh( )Ti j i jK x x x x

Examples of commonly-used kernel functions:

Polynomial kernel:

Gaussian (Radial-Basis Function (RBF) ) kernel:

Sigmoid:

Slide credit: Jinwei Gu

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SVM for image classification

• Train k binary 1-vs-all SVMs (one per class)• For a test instance, evaluate with each

classifier• Assign the instance to the class with the

largest SVM output

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Naïve Bayes

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Naïve Bayes Model

C – Class F - Features

We only specify (parameters): prior over class labels

how each feature depends on the class

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Slide from Dan Klein

Example:

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Percentage of documents in training set labeled as spam/ham


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In the documents labeled as spam, occurrence percentage of each word (e.g. # times “the” occurred/# total words).


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In the documents labeled as ham, occurrence percentage of each word (e.g. # times “the” occurred/# total words).


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Classification

The class that maximizes:

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Classification

• In practice

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Classification

• In practice– Multiplying lots of small probabilities can result in

floating point underflow

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Classification


floating point underflow– Since log(xy) = log(x) + log(y), we can sum log

probabilities instead of multiplying probabilities.

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Classification



probabilities instead of multiplying probabilities.– Since log is a monotonic function, the class with

the highest score does not change.

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Classification



probabilities instead of multiplying probabilities.– Since log is a monotonic function, the class with

the highest score does not change.– So, what we usually compute in practice is:

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Naïve Bayes on images

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Naïve Bayes

C – Class F - Features

We only specify (parameters): prior over class labels

how each feature depends on the class

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Naive Bayes Parameters

Problem: Categorize images as one of k object classes using Naïve Bayes classifier:– Classes: object categories (face, car, bicycle, etc)– Features – Images represented as a histogram of

visual words. are visual words.

treated as uniform. learned from training data – images labeled

with category. Probability of a visual word given an image category.

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Multi-class classifier –Naïve Bayes (1)

• Let V = {vi}, i = 1,…,N, be a visual vocabulary, in which each vi represents a visual word (cluster centers) from the feature space.

• A set of labeled images I = {Ii } .

• Denote Cj to represent our Classes, where j = 1,..,M

• N(t,i) = number of times vi occurs in image Ii

• Compute P(Cj|Ii):


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Multi-class Classifier –Naïve Bayes (2)

• Goal - Find maximum probability class Cj:

• In order to avoid zero probability, use Laplace smoothing:


Results

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Results

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Results

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Results

Results on Dataset 2

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Results

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Results

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Results

Thoughts?

• Pros?

• Cons?

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Related BoF modelspLSA, LDA, …

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pLSA

wordtopicdocument

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pLSA

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pLSA on images

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Discovering objects and their location in imagesJosef Sivic, Bryan C. Russell, Alexei A. Efros, Andrew Zisserman, William T. Freeman

Documents – ImagesWords – visual words (vector quantized SIFT descriptors)Topics – object categories

Images are modeled as a mixture of topics (objects).

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Goals

They investigate three areas: – (i) topic discovery, where categories are

discovered by pLSA clustering on all available images.

– (ii) classification of unseen images, where topics corresponding to object categories are learnt on one set of images, and then used to determine the object categories present in another set.

– (iii) object detection, where you want to determine the location and approximate segmentation of object(s) in each image.

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(i) Topic Discovery

Most likely words for 4 learnt topics (face, motorbike, airplane, car)

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(ii) Image Classification

Confusion table for unseen test images against pLSA trained on images containing four object categories, but no background images.

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(ii) Image Classification

Confusion table for unseen test images against pLSA trained on images containing four object categories, and background images. Performance is not quite as good.

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(iii) Topic Segmentation

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