Date post: | 02-Jan-2016 |
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Week5 Amari Lewis
Aidean Sharghi
Testing the data for classification• Divide the date into TEST and TRAIN data.
• First the regular .jpeg images
• Then, the Epipolar plane images (EPI)
Testing Training
Bike 11 13
Building 38 39
Tree 14 13
Vehicle 52 54
115 119
Rawfeatures 71289x31
Appling PCA-a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences.
Concatenating all the features by multiplying each by the coeff (pca)
Concatenating the cofeatures
Gmm – Gaussian mixture model Vl_setup [mean, covariance, priors] vl_gmm
[concatfeat,100];
• To help construct a visual word dictionary
Apply Fisher vector building the histogram to determine classification of the data
Creating labels for classification
Run libSVM
Overall accuracy = 78%
Confusion matrix
Had to normalize the results, due to the number of samples
in each category being different
Categories of regular .jpg images
labels;1- bike 81.8% 2- building 71%3- tree 64.3%4- vehicle 96.2%
For the EPIs
• The images had to be resized because there because of their massive size, the program could not run on any of the current hard drives.
• Reconstructed DenseHOG
• same method as the regular images
• Overall accuracy = 54%
For next week…
• Increasing the patch size without resizing the image..
• Try different approaches to increase the accuracy of EPIs