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