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Visual Categorization With Bags of Keypoints. Outline. Introduction Method Experiments Conclusion. Outline. Introduction Visual Categorization Is NOT Expected Goals Bag of Words Analogy Method Experiments Conclusion. Introduction. A method for generic visual categorization.

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Visual categorization with bags of keypoints

Visual CategorizationWithBags of Keypoints


Outline
Outline

  • Introduction

  • Method

  • Experiments

  • Conclusion


Outline1
Outline

  • Introduction

    • Visual Categorization Is NOT

    • Expected Goals

    • Bag of WordsAnalogy

  • Method

  • Experiments

  • Conclusion


Introduction
Introduction

  • A method for genericvisualcategorization

Face


Visual categorization is not
Visual Categorization Is NOT

  • Recognition

    • Concerns the identification of particularobject instances

Prasad

Xinwu


Visual categorization is not1
Visual Categorization Is NOT

  • Content based image retrieval

    • Retrieving images on the basis of low-level image features


Visual categorization is not2
Visual Categorization Is NOT

  • Detection

    • Decidingwhether or not a member of one visualcategoryispresent in a given image

Face Yes

Cat No


Visual categorization is not3
Visual Categorization Is NOT

  • Detection

    • Decidingwhether or not a member of one visualcategoryispresent in a given image

    • « One Visual Category » - soundssimilar

    • Yetmost of the existingdetection techniques require

      • Precisemanualalignment of the training images

      • Segregation of these images intodifferentviews

    • Bagsof Keypointsdon’tneedany of these


Expected goals
Expected Goals

  • Shouldbereadilyextendable

  • Shouldhandle the variations in view, imaging, lighting condition, occlusion

  • Shouldhandle intra class variations


Bag of words analogy
Bag of WordsAnalogy

Image Credits: Cordelia Schmid


Bag of words analogy1
Bag of WordsAnalogy

Image Credits: Li Fei Fei


Bag of words analogy2
Bag of WordsAnalogy

Image Credits: Li Fei Fei


Bag of words analogy3
Bag of WordsAnalogy

  • Zhu et al – 2002 have usedthismethod for categorizationusingsmall square image windows – calledkeyblocks

  • But keyblocksdon’t posses any invariance propertiesthatBagsof Keypointsposses


Outline2
Outline

  • Introduction

  • Method

    • Detection And Description of Image Patches

    • Assignment of Patch Descriptors

    • Contruction of Bag of Keypoints

    • Application of Multi-Class Classifier

  • Experiments

  • Conclusion


Method
Method

  • 4 main steps

    • Detection And Description of Image Patches

    • Assignment of Patch Descriptors

    • Contruction of Bag of Keypoints

    • Application of Multi-Class Classifier

      • Categorization by Naive Bayes

      • Categorization by SVM

  • Designed to maximize classification accuracywhileminimizingcomputational effort


Detection and description of image patches
Detection And Description of Image Patches

  • Descriptorsshouldbe invariant to variation but have enough information to discriminatedifferentcategories

Image Credits: Li Fei Fei


Detection and description of image patches1
Detection And Description of Image Patches

  • Detection – Harris affine detector

    • Last presentation by Guru and Shreyas

  • Description – SIFT descriptor

    • 128 dimensionalvector – 8 * (4*4)


Assignment of patch descriptors
Assignment of Patch Descriptors

  • When a new query image isgiven, the deriveddescriptorsshouldbeassigned to onesthat are already in our training dataset

  • Check themwith

    • All the descriptorsavailable in the training dataset – tooexpensive

    • Only a few of them – but not too few

  • The number of descriptorsshouldbecarefullyselected


Assignment of patch descriptors1
Assignment of Patch Descriptors

  • Each patch has a descriptor, whichis a point in somehigh-dimensionalspace (128)

Image Credits: K. Grauman, B. Leibe


Assignment of patch descriptors2
Assignment of Patch Descriptors

  • Close points in feature space, means similar descriptors, which indicates similar local content

Image Credits: K. Grauman, B. Leibe


Assignment of patch descriptors3
Assignment of Patch Descriptors

  • To reduce the hugenumber of descriptorsinvolved (600 000), they are clustered

  • Using K-means

    K-meansisrunseveral times usingdifferent K values and initial positions

    One with the lowestempiricalriskisused

Image Credits: K. Grauman, B. Leibe


Assignment of patch descriptors4
Assignment of Patch Descriptors

  • Now the descriptorspace looks like

    Featurespaceisquantized

    These cluster centers are the prototype words

    Theymake the vocabulary

Image Credits: K. Grauman, B. Leibe


Assignment of patch descriptors5
Assignment of Patch Descriptors

  • When a query image comes

    Itsdescriptors are attached to the nearest cluster center

    That particularwordispresent in the query image

Image Credits: K. Grauman, B. Leibe


Assignment of patch descriptors6
Assignment of Patch Descriptors

  • Vocabularyshouldbe

    • Large enough to distinguish relevant changes in the image parts

    • Not so large that noise startsaffecting the categorization


Contruction of bags of keypoints
Contruction of Bagsof Keypoints

  • Summarize entire image based on its distribution (histogram) of word occurrences

Image Credits: Li Fei Fei


Application of multi class classifier
Application of Multi-Class Classifier

  • Apply a multi-class classifier, treat the bag of keypoints as the featurevector, thusdeterminewhichcategory or categories to assign to the image

    • Categorization by Naive Bayes

    • Categorization by SVM


Categorization by naive bayes
Categorization by Naive Bayes

  • Can be viewed as the maximum a posteriori probability classifier for a generative model

  • To avoidzeroprobabilities of , Laplace smoothingisused


Categorization by svm
Categorization by SVM

  • Find a hyperplane which separates two-class data with maximal margin


Categorization by svm1
Categorization by SVM

  • Classification function:

    f(x) = sign(wTx+b) where w, b parameters of the hyperplane


Categorization by svm2
Categorization by SVM

  • Data sets not always linearly separable

    • error weighting constant to penalizes misclassification of samples in proportion to their distance from the classification boundary

    • A mapping φis made from the original data space of X to another feature space

      This isused in Bag of Keypoints


Categorization by svm3
Categorization by SVM

  • What do youmean by mappingfunction?


Categorization by svm4
Categorization by SVM

  • Can be formulated in terms of scalar products in the second feature space, by introducing the kernel

  • Then the decision function becomes


Outline3
Outline

  • Introduction

  • Method

  • Experiments

  • Conclusion


Experiments
Experiments

  • Somesamplesfrom the inhousedataset


Experiments1
Experiments

  • Impact of the number of clusters on classifier accuracy and evaluate the performance of

    • Naive Bayes classifier

    • SVM

  • Three performance measures are used

    • Confusion matrix

    • Overallerror rate

    • Meanranks



Results1
Results

  • For K = 1000

    • Naive Bayes 28% SVM 15%


Experiments2
Experiments

  • Performance of SVM in anotherdataset



Results3
Results

  • Multiple objects of the samecategory/ partial view

  • Misclassifications


Outline4
Outline

  • Introduction

  • Method

  • Experiments

  • Conclusion

    • Future Work


Conclusion
Conclusion

  • Advantages

    • Bag of Keypointsis simple

    • Computationally efficient

    • Invariant to affine transformations, occlusions, lighting, intra-class variations


Future work
Future Work

  • Extend to more visualcategories

  • Extend the categorizer to incorporategeometric information

  • Make the methodrobustwhen the object of interestisoccupyingonly a small fraction of the image

  • Investigatemany alternatives for each of the four steps of the basic method


Q&A

Thank You


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