1. Color-Attributes-Related Image Retrieval
Student: Kylie Gorman
Mentor: Yang Zhang

2. Fix Code Initially calculating PCA and GMM independently
Calculate GMM based on PCA results

3. Steps

4. Improved Steps

5. Compare New and Previous Results
Improved Results
Compare New and Previous Results

6. HSV Results
Original Average: ~25%
New Average: ~45%

7. RGB Results
Original Average: ~20%
New Average: ~50%

8. CIELAB Results
Original Average: ~17%
New Average: ~42%

9. Combined Results Average: ~9% Early Fusion Did Not Work
Possibly Requires Debugging

10. New sets to include separate attributes such as object recognition
New Data Sets
New sets to include separate attributes such as object recognition

11. New Data Birds 200 Flowers 102 Cartoon
200 species/categories with 6,033 images total
Flowers 102
102 categories with images per category
8189 images total
Cartoon
590 images total

12. Flowers 102 Part One Part Two Part Three
Get Feature Matrices with Color Moments
Calculate PCA and GMM of training data: 1,020 images
Part Two
Get Feature Matrices with Dense SIFT
Calculate PCA and GMM of training data: 100 images
Part Three
Use new Color Descriptor

13. Future Goals
Compare our color moment plus Dense SIFT against new color descriptor and Dense SIFT
If no improvement, determine why
Follow same steps with Bird 200 data set