Amari Lewis Aidean Sharghi Week 8 - Amari Lewis Aidean Sharghi

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)

Testing for classification- same process as before

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

Using DCT feature vector for EPIs- 96%

Light field regular (jpg) images…

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

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

7 blocks representing the same line from each of the images

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

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

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