1. AN EVALUATION OF LOCAL IMAGE FEATURES FOR OBJECT CLASS RECOGNITION
Presented by: Centre for Computational Intelligence
School of Computer Engineering
AN EVALUATION OF LOCAL IMAGE FEATURES FOR OBJECT CLASS RECOGNITION
The use of local image features (LIF) for object class recognition is becoming increasingly popular. To better understand the suitability and power of existing LIFs for object class recognition, a simple but useful method is proposed in evaluation of such features. We have compared the performance of eight frequently used LIFs by the proposed method on two popular databases. We have used F-measure criterion for this evaluation. It is found that the individual performance of SURF and SIFT features are better than that of the global features on ETH-80 database with considerably lower number of training objects. However, it may not be good enough for more challenging object class recognition problem (e.g. Caltech-101). The evaluation of LIFs suggests the requirement for further investigation of more complementary LIFs.
Acronym
Name
Dimension
SIFT
Scale Invariant Feature Trans
128
SURF
Speed-Up Robust Features 64
GLOH
Grad Location Orientation Hist GM
Gradient Moment 20 SF
Steerable Filters 14
CMI
Color Moment Invariant 18
RMI
Revised Moment Invariant 15
Approximation of Bayesian Classification
Training
Extract all features (d) of each class
One k-d tree for each class.
NN classification
dj, c compute NN of dj in C: NNC(dj)
List of features: Invariant to affine geometric
and photometric transformations
Method: An approximation of Bayesian
optimal classifier (Boiman et al. 08)
SIFT
SURF
GLOH GM SF
CMI
RMI
Apple
0.95
0.99
0.89
0.84
0.10
0.70
0.04
Car
1.00
0.96
0.62
0.15
Cow
0.98
0.94
0.83
0.46
0.24
Cup
0.67
0.05
Dog
0.90
0.61
0.79
0.21
Horse
0.97
0.91
0.86
0.66
0.17
Pear
0.41
0.09
Tomato
0.23
Avg
0.984
0.43
0.12
Results: Wide baseline matching (previously
seen objects with viewpoint change)
Databases: ETH-80 and Caltech-101
Results: Recognition rate of previously unseen objects.
(different number of training images were used)
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