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Week5 Amari Lewis Aidean Sharghi. Testing the data for classification Divide the date into TEST and...

Date post: 02-Jan-2016
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Week5 Amari Lewis Aidean Sharghi
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Page 1: 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.

Week5 Amari Lewis

Aidean Sharghi

Page 2: 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.

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

Page 3: 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.

Rawfeatures 71289x31

Page 4: 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.

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

Page 5: 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.

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

Page 6: 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.

Concatenating the cofeatures

Page 7: 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.
Page 8: 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.

Gmm – Gaussian mixture model Vl_setup [mean, covariance, priors] vl_gmm

[concatfeat,100];

• To help construct a visual word dictionary

Page 9: 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.

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

Page 10: 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.

Creating labels for classification

Page 11: 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.

Run libSVM

Overall accuracy = 78%

Page 12: 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.

Confusion matrix

Had to normalize the results, due to the number of samples

in each category being different

Page 13: 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.

Categories of regular .jpg images

labels;1- bike 81.8% 2- building 71%3- tree 64.3%4- vehicle 96.2%

Page 14: 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.

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%

Page 15: 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.

For next week…

• Increasing the patch size without resizing the image..

• Try different approaches to increase the accuracy of EPIs


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