Transcript Week 3 Review

COLOR-ATTRIBUTESRELATED IMAGE
RETRIEVAL
Student: Kylie Gorman
Mentor: 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
Related Papers
• Van De Weijer, Joost, et al. "Learning color names for real-world
applications."Image Processing, IEEE Transactions on 18.7 (2009):
1512-1523.
• Keen,Noah. “Color Moments”(2005).
• Shahbaz Khan, F., et al. "Color attributes for object
detection." Computer Vision and Pattern Recognition (CVPR), 2012
IEEE Conference on. IEEE, 2012.
Background
• Color moments: measures that differentiate images based on color
• 3 moments per box based on mean, standard deviation, and
skewness
• Higher difference of color moments means less similar than two
images with lower DOM value
• Learning color names from real-world images is more useful than
chip-based color mapping
• Using color attributes in conjunction with object detection provides
more accurate results
Beginning Code
• Separates an HSV image into boxes and calculates the color
moments of each box
• One moment for mean, standard deviation, and skewness
• My contribution:
•
•
•
•
Function for HSV images should also take in corresponding binary images
Break up binary image into corresponding blocks of HSV image
Locate parts of image that contain desired object
Concatenate these boxes into a single matrix to isolate object and only
take the boxes from HSV image that contain the object
Further Contribution
• Use Google images and calculate feature matrix of HSV image
• Concatenate all of the matrices calculated and use PCA (Principal
Component Analysis) function in MATLAB
• PCA can be used for image compression
• Store result and multiply by each individual feature matrix
Current Contribution
• Mixture Model: a probabilistic model for representing the presence
of subpopulations within an overall population
• Gaussian Mixture Model: a mixture of K multivariate Gaussian
distributions.
• GMM clusters the existing points
• [means, covariances, priors] = vl_gmm(data, numClusters);
Fisher Vector
• Since all of the data consists of real-world images, all of the images
have different number of vectors
• Fisher vector will allows us to quantize the vectors
• Similar to Bag of Words: will compare local descriptors to a
dictionary obtained with GMM
• Different than Bag of Words: instead of storing visual word
occurrences, stores difference between dictionary elements and
pooled local features
• Compute Fisher Vector encoding with VLFEAT MATLAB interface
• encoding = vl_fisher(datatoBeEncoded, means, covariances, priors);
Current Progress and Future Plans
• Read related papers and understand background concepts
• Validate current code with small data set of real-world images
• Add more features to the system to improve its performance
• Apply code to larger data set
• Collecting Dataset: There are not any existing color image datasets on the
Internet. Use automatic image collecting tool to create our own color
object dataset.
• Also include object detection
• Possible Bonus: Implement novel ideas about general attribute
image retrieval system. Determine if it is effective or not.