Feature Hierarchies for Object Classification

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FEATURE HIERARCHIES FOR OBJECT CLASSIFICATION By: Eng Wei Yong, Rui Hua, Vanya V.Valindria

description

Object classification using the feature hierarchies method, as bio-inspired

Transcript of Feature Hierarchies for Object Classification

Page 1: Feature Hierarchies for Object Classification

FEATURE HIERARCHIES FOR OBJECT CLASSIFICATION

By: Eng Wei Yong, Rui Hua, Vanya V.Valindria

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OUTLINE

1. Introduction2. Comparison with previous work3. Algorithm4. Experiment and Results5. Conclusion

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Introduction

Automatically extracting informative feature hierarchies for object classification

Top-down manner Entire hierarchy are

learned during a training phase

Feature Hierarchies for Object Classification

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Overview of Feature Hierarchy Hierarchies are significantly more

informative compared with holistic features. Selection of effective image features is

crucial Identify common object parts Allows variations learned from training data

Input: A set of class & non-class images Output: Hierarchical features with learned

parameters

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Previous Work

Non-hierarchical Feature hierarchies

Architecture of the hierarchy is pre-defined Advantages of both method are

combined in this paper

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Construction of Feature Hierarchies

Algorithm

Initial informative fragments are selected Selected fragments are used to extract

the sub-features Optimize parameters of features

hierarchy Classification

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Selecting informative image fragment

Detection threshold, for each fragment is selected to maximize MI(fi;C)

Identifies next fragment that delivers maximal amount of additional information

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Extracting sub-fragments

• Positive examples are thus fragments in class positive images where the feature is detected or almost detected

• Negative examples are fragments in class negative images where the feature is detected or almost detected

Constructing Positive and negative examples

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Extracting sub-fragments

Parent fragment

Child fragment

If it increases delivered information

Keep decomposition

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Extracting sub-fragments

Grand parent fragment

Parent fragment

Child fragment

If it does NOT increases delivered information

Stop decomposition

Atomic fragment

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Optimizing ROI

Size of ROI

ROI too smallinformation lowROI too largeinformation low

The size of ROI should be chosen to maximize the mutual information between the fragment and the class

Top-down manner

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Classification by hierarchy

The response of all sub-features

Final response

At top level, compare Sp with 0

-1< Sp <1

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Classification by hierarchy

During training updating weights and positions alternatively

Position step: Fixed weights Optimize positions Weight step:

Fixed position

Optimize weights

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Summary of algorithm

Hierarchical Feature Construction

Positive Images

Negative Images

S(f)

Evaluate MI

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Summary of algorithm Hierarchical Feature Construction

Positive Images

Negative Images

H

Atom

Optimize ROI

S(f)

Evaluate MI

Hierarchical Feature Construction

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Summary of algorithm

Classification Step

Novel Image Hierarchy

Cross-correlation

Response Map

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Summary of algorithm

Classification Step

Hierarchy

Sub-feature

Response Map

Feature Response Map

TOP: Final Response

> 0

< 0

1 0

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Experiment

3 object classes: faces, cows and airplanes

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Experiment

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Conclusions

Pros: The extraction of image fragments is automatic The hierarchies outperforms the holistic features Feature hierarchies can be used to improve the

performance of classification schemes

Cons: Optimization of features is not quite complete Application process is not as computationally

efficient

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THANK YOU…..