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CSE468: Computer Vision
Assignment 1

1 Introduction
For this assignment, you will be required to implement and analyze a naive
1-NN based image classifier’s performance when using different features.
2 NaiveClassifier
Create a class N aiveClassif ier which accepts the following in its constructor ( init ) in the following order
1. Training images as a (number of images, number of channels, x dim,
y dim) numpy array. For now assume every image in the training set
should have the same number of channels and the same dimensions.
2. Training labels. This should be a (number of images, 1) numpy array.
3. An object of type FeatureExtractor
You should also implement the following methods. The names are self
explanatory.
1. extract feature from single image, which should accept an image as parameter. The image is a (number of channels, x dim, y dim) numpy
array. This method should return the feature extracted by calling the
extract feature method of the FeatureExtractor object.
2. extract feature from multiple images, which should accept a (number of images,
number of channels, x dim, y dim) numpy array as parameter and return a (number of images, feature dim) array.
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3. classify single image, which will accept an image as parameter and
return a label value and the corresponding score.
4. classify multiple images, which will accept multiple images as parameter and return a list of label values and a list of scores.
3 ClassificationMetrics Class
This class should have the following methods:
1. init : This method should accept a list of training labels, a list of
predicted labels, and a list of predicted scores as parameter. It should
also construct the confusion matrix and cache this information.
2. get confusion matrix for heatmap: Returns a 0 − 1 normalized confusion matrix.
3. calculate accuracy: Calculate and return the accuracy of the predictions. It should return a value in the range 0 − 1.
4. calculate precision:Calculate and return the average precision.
5. calculate recall:Calculate and return the average recall.
6. calculate f1 :Calculate and return the F1 score.
7. calculate roc values:This method should accept a list of thresholds as
parameter and then calculate and return the ROC values (tp, fp) in
a list of lists. A list per threshold value per class.
8. calculate lift values: Similar to calculate roc values, but calculates lif t
values.
4 FeatureExtractor class
This is a very simple class which has a single method extract feature, which
accepts an image numpy array as parameter, extracts the feature from it and
returns the image descriptor as a numpy array.
For the FeatureExtractor class, the extract feature method should simply
return a vectored form of the image.
You should also implement the following sub-classes of FeatureExtractor
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1. ColourHistogramExtractor
2. HoGExtractor
3. SIFTBoVWExtractor : The constructor of this class should accept a
(number of images, number of channels, x dim, y dim) numpy array.
You should use this to calculate the visual words while constructing
the object.
5 Documentation
You are required to write a report with the following sections.
1. Introduction: Overview of your work and results.
2. Literature Review: Detailed, wherever possible mathematical, description of 1-NN, Metrics, Features.
3. Experimental setup: Description of the data set, how it was partitioned, how the comparison of the performance of the different features
were done and what metrics were calculated.
4. Resutls and discussion: Presentation of the results in a sensible and
readable format. Just presentation is not enough. You will need to
analyze the results, especially failure cases on a per-feature basis and
provide your justified insights why things work and why things do not
work.
5. Conclusion: The conclusion of your report
Please note that to write the report you will be required to perform
the classifications using the different features. This will require some driver
code. You should be writing the driver code and submiting it as well. Please
also note the report must be written in LATEX. Do not forget to include your
names as authors of the report.
6 Submission Instructions
Submission is due by 9am of the 18-02-2019. Your code,documentation
(PDF and LATEXsource) should be zipped in a single zip file and emailed
to me at [email protected] with the subject line “CSE468
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Spring 2019 Assignment 1”. Please mention your group member names and
student id numbers in the body of the email.
Also, please note that as discussed in class, you should be writing all the
code yourself, without using the help of libraries. The only exception is the
use of image loading/writing API and the numpy library. You can also use
graph plotting libraries only for the purpose of plotting graphs.
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