Color-Attributes-Related Image Retrieval Student: Kylie Gorman Mentor: Yang Zhang.
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Transcript of Color-Attributes-Related Image Retrieval Student: Kylie Gorman Mentor: Yang Zhang.
Color-Attributes-Related Image Retrieval
Student: Kylie GormanMentor: Yang Zhang
Problem and Solution•Content based image retrieval is a common
problem in computer vision•Object-related image retrieval is a popular area
related to this issue•Attributed-related image retrieval is a possible
solution •Enable a person to retrieve an image based on
attributes of an object•Some people have tried to use color as a starting
point, but this is still a very novel concept
Data Sets•Learn colors from real-world images•Train data: Google data set
▫11 colors with 100 images per color•Test data: EBay data set
▫11 colors with 12 images per color▫Corresponding binary image for each
image
Train Data Steps• Calculate feature matrix based on Color
Moments▫Calculate every box rather than every pixel
• Concatenate feature matrices• Calculate PCA (Principal Component Analysis)• Calculate GMM (Gaussian Mixture Model) based
on PCA results• Multiply individual feature matrices by
coefficient matrix• Use GMM results to calculate Fisher Vectors• Train 11 SVM’s
Test Data Steps•Calculate feature matrix of each image,
isolating the object first using binary images
•Use PCA and GMM results from training data to calculate fisher vectors
•Apply Fisher Vector to each individual result to obtain vectors that are the same size
•Classify eBay images using 11 SVM’s from training data
•Calculate Precision
Steps
CIELAB Results
Average Precision:
~42%
HSV Images
Average Precision: ~45%
RGB Images
Average Precision: ~50%
New Data Sets•Birds 200
▫200 species/categories with 11,788 images total
•Flowers 102▫102 categories with 40-258 images per
category▫8189 images total
•Cartoon▫590 images total
Flowers 102 and Birds 200•Part One
▫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
•Incorporate object detection and image retrieval