Multiple View Based Multiple View Based 3D Object Classification 3D Object Classification

Using Ensemble Learning of Using Ensemble Learning of Local Subspaces (Local Subspaces (ThBT4.3ThBT4.3))

Jianing Wu, Kazuhiro Fukui

lacarte@cvlab.cs.tsukuba.ac.jp,kfukui@cs.tsukuba.ac.jp

Graduate school of Systems and Information Engineering

University of Tsukuba (Japan)

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AbstractAbstract

We proposed a statistical method for object classification based on multi-view.

The proposed method is an extension of MSM.

Problems of previous works (MSM, KMSM) has been solved.

We evaluated the classification performance of the proposed method and previous works.

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Table of ContentsTable of Contents

Backgrounds Multi-view classification and problems of existing

methods for nonlinear distribution

The proposed method Approximation by local subspaces and ensemble

learning

Experimental results Performance comparison using multi-view images of

objects

Summary

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Multi-view Based Object ClassificationMulti-view Based Object Classification

Multiple view is more beneficial for object classification than single.

View based approach does not need 3D model for classification.

Subspace related methods has been proposed for multi-view frame work.

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Existing view based approachesExisting view based approaches

Mutual Subspace Method (MSM)O.Yamaguchi, K.Fukui, K.Maeda: Face recognition using temporal image sequence. Proc.IEEE 3rd International Conference on Automatic Face and Gesture Recognition, pp.318-323,1998.

ProLow computation cost

ConCannot handle nonlinear distribution

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Multi-view Based Object ClassificationMulti-view Based Object Classification

Feature vectors from multi-view input are likely to have nonlinear distribution.

Subspace approximation therefore is not accurate.

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Existing view based approachesExisting view based approaches

Kernel Mutual Subspace Method (KMSM)H.Sakano, N.Mukawa: Kernel mutual subspace method for robust facial image recognition. Proc. 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, Vol.1, pp.245-248, 2000.

Project feature vectors to a high dimensional space to reduce their nonlinearity.

Pros: Can handle nonlinear distribution.

Cons: Consume more computation time, and this increases

at order N^2 as learning data increases. Some critical parameters need to be optimized

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Motivation of the Proposed MethodMotivation of the Proposed Method

Approximate nonlinear distribution. Divide the original distribution, and

approximate each subset.

Achieve comparable classification performance with KMSM, using less computation. Perform classification without kernel framework.

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Table of ContentsTable of Contents

Backgrounds Multi-view classification and problems of existing

methods for nonlinear distribution

The proposed method Approximation by local subspaces and ensemble

learning

Experimental results Performance comparison using multi-view images of

objects

Summary

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The Proposed MethodThe Proposed Method Divide the nonlinear distribution into several

subsets based on Euclidean distance. Nonlinearity of each subset is weaker.

We approximate each subset with local subspace.

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Ensemble ClassificationEnsemble Classification

Division number and dimension of each local subspace are parameters affect classification performance.

We construct local subspaces under multiple combination.

We assume each case as weak classifier and apply ensemble learning.

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The Proposed Method with WeightThe Proposed Method with Weight Each local subspace carries a weight coefficient based

on its classification performance. This coefficient weights the canonical angle. The coefficients are simply the accuracy rate of each

local subspace in preliminary experiments.

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The Proposed MethodThe Proposed Method

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Table of ContentsTable of Contents

Backgrounds Multi-view classification and problems of existing

methods for nonlinear distribution

The proposed method Approximation by local subspaces and ensemble

learning

Experimental results Performance comparison using multi-view images of

objects

Summary

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Classification ExperimentClassification Experiment

Compare the classification performance and computation cost of MSM, KMSM and the proposed method.

Use multi-view image data set ‘The ETH-80 Image Set’B.Leibe, B.Schiele: Analyzing appearance and contour based methods for object categorization. Proc. CVPR'03, Vol.2, pp.409-415, 2003

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Classification ExperimentClassification Experiment The dataset contains 8 classes, 10 objects

for each class. 41 points of

view for each object

Feature vector is the resized image (16x16)

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Classification ExperimentClassification Experiment

Use 164 images to learn for each class. (generate dictionary)

Evaluation input is images from an unknown object. (10 images/points of view)

By exchanging learning data and evaluation data (leave-one-out), we repeated experiment for 1640 times.

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Classification ExperimentClassification Experiment

Classification performance of each method

Method Accuracy Rate(%)

Separability

EER(%)

MSM 69.5 0.34 20

KMSM 87.2 0.41 15

Proposed method

86.5 0.44 14

Proposed method with weight

94.7 0.55 9

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Classification ExperimentClassification Experiment

The proposed method improved classification performance from MSM

The proposed method achieved comparable performance with KMSM

Classification performance of the proposed method was improved by introducing weight to each local subspace

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Classification ExperimentClassification Experiment

Computation cost of each method

Method Calculation Time (seconds per input)

Order

MSM 0.1 O(n)

Proposed method

0.4 O(n)

Proposed method with weight

0.4 O(n)

KMSM 3.1 O(n^2)

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Classification ExperimentClassification Experiment

The proposed method consumes less computation time compared with KMSM

The computation time of the proposed method increases in order N as learning data increases

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Classification ExperimentClassification Experiment Classification performance of weak classifiers

Proposed method

86.5 0.44 14

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Table of ContentsTable of Contents

Backgrounds Multi-view classification and problems of existing

methods for nonlinear distribution

The proposed method Approximation by local subspaces and ensemble

learning

Experimental results Performance comparison using multi-view images of

objects

Summary

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SummarySummary

We proposed a method to achieve comparable performance with KMSM by less calculation.

Classification performance is further improved by introducing weight to each local subspace

The advantages of the proposed method is shown with classification experiment of objects.

Thank YouThank You

Multiple View Based 3D Object Classification Using Ensemble Learning of Local

Subspaces (ThBT4.3)

Jianing Wu, Kazuhiro Fukui