1. Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Presented by: Lubomir Bourdev
Many of the slides by: Svetlana Lazebnik

2. Key Idea Pyramid Match Kernel (Grauman & Darrell)
Pyramid in feature space, ignore location
Spatial Pyramid (this work)
Pyramid in image space, quantize features

3. Algorithm Extract interest point descriptors (dense scan)
Construct visual word dictionary
Build spatial histograms
Create intersection kernels
Train an SVM

4. Algorithm OR Extract interest point descriptors (dense scan)
Construct visual word dictionary
Build spatial histograms
Create intersection kernels
Train an SVM OR
Weak (edge orientations)
Strong (SIFT)

5. Algorithm Extract interest point descriptors (dense scan)
Construct visual word dictionary
Build spatial histograms
Create intersection kernels
Train an SVM
Vector quantization
Usually K-means clustering
Vocabulary size (16 to 400)

6. Algorithm Extract interest point descriptors (dense scan)
Construct visual word dictionary
Build spatial histograms
Create intersection kernels
Train an SVM

7. Algorithm Extract interest point descriptors (dense scan)
Construct visual word dictionary
Build spatial histograms
Create intersection kernels
Train an SVM

8. Algorithm Extract interest point descriptors (dense scan)
Construct visual word dictionary
Build spatial histograms
Create intersection kernels
Train an SVM

16. My experiment: Butterfly Classification
Peacock
Zebra

17. Butterflies Dataset from Lazebnik / Schmid / Ponce 70 train / 64 test
Images centered on the butterfly
Significant background clutter
Large pose/viewpoint variations
Scale variations: up to x4

18. Butterfly Results Spatial pyramid levels: 1 (No pyramid)
Linear
Intersection
Weak (16)
82.6%
Strong (200)
81.9%
89.5%
Dims 16
200
Spatial pyramid levels: 4
Linear
Intersection
Weak (16)
88.6%
86.7%
Strong (200)
84.8%
89.5%
Dims
1360
17000