1. Amari Lewis Aidean Sharghi
Week 8 -
Amari Lewis
Aidean Sharghi

2. Testing the Switzerland dataset
Implementing DCT- Discrete Cosine Transform
Steps:
Separate the RGB into 3 channels
Calculate the row-wise mean- calculates the mean of each row to create a vector
Calculate the DCT for each channels
Concatenate some coefficients, using as a feature vector (smaller)

3. Testing for classification- same process as before

4. Regular Images (JPEG) 97% Using HOG and Fisher vector

5. Using DCT feature vector for EPIs- 96%

6. Light field regular (jpg) images…

7. Confusion matrix- regular images
Categories-
Bikes- 67%
Buildings- 94%
Trees- 100%
Vehicles- 89%
Overall accuracy- 80%

8. Retesting the original light field images for classification
Implementing a code to take each of the 7 different images and concatenate them to form each line (1080) of the image as EPI lines.
Using the same method as the Switzerland dataset

9. 7 blocks representing the same line from each of the images

11. Overall – 77% Confusion matrix for EPIs Bike- 80% Building-100%
Tree-75%
Vehicle- 56%
Overall – 77%

12. Works better when the EPIs are extracted from each line of the image separately.
Also as a result of using a smaller feature vector the detail

13. Conclusions First method Light field EPIs New method
Light field images (jpeg)
Previous results..
78%
Light field EPIs
-resized the EPI because they are too large 54%
-increasing the patch size window 74%
New method
Switzerland dataset;
EPI 96%
Regular images 97%
Light field dataset
EPI- 77%
Regular images 80%
Achieved one of our goals from the previous weeks which was to increase the overall accuracy of 74%
Conclusions