Dense Color Moment: A New Discriminative Color Descriptor Kylie Gorman, Mentor: Yang Zhang...
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Transcript of Dense Color Moment: A New Discriminative Color Descriptor Kylie Gorman, Mentor: Yang Zhang...
Dense Color Moment: A New Discriminative Color Descriptor
Kylie Gorman, Mentor: Yang Zhang
University of Central Florida
I. Problem:
Create Robust Discriminative Color Descriptor Color Descriptor Significance
Large variations in RGB values occur due to scene accidental events
II. Previous Methods:
III. Our Approach:
V. Experiments: Color Moment
Training Data
Google Data Set: 1,100 images
Testing Data
EBay Data Set: 4 categories
12 images per color, 132 images
per category
Color Moment and Dense SIFT
Birds 200 (20 classes)
200 species/categories with 11,788 images total
Color HistogramColor Mapping
Blockwise Color Moment Feature Incorporate the spatial context
information More complete representation of
color in an image than pixel color value. We use three moments to describe mean, variance and degree of asymmetry of a color distribution.
Color Moment Calculations
Advantages:
1. “Colors” themselves are chromatic distribution. Quantized color
descriptor ,which is based on distribution, can better represent color in images.
2. Instead using color means only, we also introduce two other moments.
3. Color moment can be used as the discriminative descriptor and is effective in
a general classification problem
Training StepsCalculate feature matrix based on Color MomentsCalculate every box rather than every pixelConcatenate feature matricesCalculate PCA (Principal Component Analysis)Calculate GMM (Gaussian Mixture Model) based on PCA resultsMultiply individual feature matrices by coefficient matrixUse GMM results to calculate Fisher VectorsTrain SVMs
IV. Pipeline:
Testing StepsCalculate feature matrix of each image, isolating the object first using binary
imagesUse PCA and GMM results from training data to calculate fisher vectorsApply Fisher Vector to each individual result to obtain vectors that are the same
sizeClassify images using SVM’s from training dataCalculate Precision
V. Results:
VI. Conclusions:
CIELAB: 42% Accuracy HSV: 45% Accuracy RGB: 50% Accuracy
Classification using Color Moment performance on Google and EBay Datasets
Classification using Color Moment vs. Dense SIFT on Birds 200 Dataset
Our Color Descriptor: Accuracy = 21.3592% (110/515)
Dense SIFT: Accuracy = 21.7476% (112/515)
Color Moment and Dense SIFT Combined: Accuracy: 25.44%
Our Color Descriptor showed the same accuracy as the Dense SIFT and therefore possesses the same discriminative ability
When both features were fused, accuracy increased by 4%. This indicates that color moment representation and shape representation, like SIFT, are complementary.
Future Work Complete Color Moment and Dense SIFT with all 200 classes Increase/ Decrease Block Size Incorporate Object Detection and Image Retrieval
Acknowledgments:
Thank you to the NSF for funding the REU program for the University of Central Florida.
Also, thanks to Dr. Shah and Dr. Lobo for overseeing the program.
Mean Standard Deviation Skew
𝐸𝑖=∑𝑁
𝑗=1
( 1𝑁 𝑃 𝑖𝑗)
Color Mapping Methods
Color Histogram Method
Hue
Ligh
tness