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

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

Steps

Improved Steps

Compare New and Previous Results Improved Results Compare New and Previous Results

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

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

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

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

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

New Data Birds 200 Flowers 102 Cartoon 200 species/categories with 6,033 images total Flowers 102 102 categories with 40-258 images per category 8189 images total Cartoon 590 images total

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

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