Feature Hierarchies for Object Classification
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Transcript of Feature Hierarchies for Object Classification
FEATURE HIERARCHIES FOR OBJECT CLASSIFICATION
By: Eng Wei Yong, Rui Hua, Vanya V.Valindria
OUTLINE
1. Introduction2. Comparison with previous work3. Algorithm4. Experiment and Results5. Conclusion
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
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
Previous Work
Non-hierarchical Feature hierarchies
Architecture of the hierarchy is pre-defined Advantages of both method are
combined in this paper
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
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
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
Extracting sub-fragments
Parent fragment
Child fragment
If it increases delivered information
Keep decomposition
Extracting sub-fragments
Grand parent fragment
Parent fragment
Child fragment
If it does NOT increases delivered information
Stop decomposition
Atomic fragment
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
Classification by hierarchy
The response of all sub-features
Final response
At top level, compare Sp with 0
-1< Sp <1
Classification by hierarchy
During training updating weights and positions alternatively
Position step: Fixed weights Optimize positions Weight step:
Fixed position
Optimize weights
Summary of algorithm
Hierarchical Feature Construction
Positive Images
Negative Images
S(f)
Evaluate MI
Summary of algorithm Hierarchical Feature Construction
Positive Images
Negative Images
H
Atom
Optimize ROI
S(f)
Evaluate MI
Hierarchical Feature Construction
Summary of algorithm
Classification Step
Novel Image Hierarchy
Cross-correlation
Response Map
Summary of algorithm
Classification Step
Hierarchy
Sub-feature
Response Map
Feature Response Map
TOP: Final Response
> 0
< 0
1 0
Experiment
3 object classes: faces, cows and airplanes
Experiment
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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