1. Week5 Amari Lewis Aidean Sharghi

2. Testing the data for classification Divide the date into TEST and TRAIN data. First the regular.jpeg images Then, the Epipolar plane images (EPI) TestingTraining Bike 1113 Building 3839 Tree 1413 Vehicle 5254 115119

3. Rawfeatures 71289x31

4. Appling PCA- a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences.

5. Concatenating all the features by multiplying each by the coeff (pca)

6. Concatenating the cofeatures

8. Gmm – Gaussian mixture model Vl_setup [mean, covariance, priors] vl_gmm [concatfeat,100]; To help construct a visual word dictionary

9. Apply Fisher vector building the histogram to determine classification of the data

10. Creating labels for classification

11. Run libSVM Overall accuracy = 78%

12. Confusion matrix Had to normalize the results, due to the number of samples in each category being different

13. Categories of regular.jpg images labels; 1- bike 81.8% 2- building 71% 3- tree 64.3% 4- vehicle 96.2%

14. 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%

15. For next week… Increasing the patch size without resizing the image.. Try different approaches to increase the accuracy of EPIs