Transcript Week 9 - UCF Center for Research in Computer Vision

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
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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
Overall
accuracy- 80%
Categories-
Bikes- 67%
Buildings- 94%
Trees- 100%
Vehicles- 89%
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
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

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Light field images (jpeg)

Previous results..
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78%
Light field EPIs

Previous results..

-resized the EPI because they are too large 54%

-increasing the patch size window 74%

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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%