This project showcases various techniques for feature represenations and classification of images. We are using a sample data set of 15 classes with 1500 images as a training set, and another 1500 images as a test set.

Part 1 - Tiny Images and KNN

Implemented exactly as instructed in the rubric and the stencil code. Tiny images uses normalization and mean centering. KNN matching uses K = 13 and L2 norm for the Tiny Image features.

Accuracy (mean of diagonal of confusion matrix) is 0.239
Elapsed time is 12.958442 seconds.

Part 2 - Bag of SIFT and KNN

Implemented exactly as instructed in the rubric and the stencil code. Bag of SIFT features are found with a step size of 3 and with the "fast" parameter, uses kdtrees for nearest neighbour search. KNN matching uses K = 5 and L2 norm for the Bag of SIFT features.

Accuracy (mean of diagonal of confusion matrix) is 0.521
Elapsed time is 171.256649 seconds.

Part 3 - Bag of SIFT and SVM

Implemented exactly as instructed in the rubric and the stencil code. Linear SVM uses a LAMBDA of 0.00001.

Accuracy (mean of diagonal of confusion matrix) is 0.667
Elapsed time is 1.074299 seconds.

Part 4 - Extra Credit

Firstly, I decided to add a layer of scale (1/2x) to the vocabulary and the features. This resulted in a slight improvement.

Accuracy (mean of diagonal of confusion matrix) is 0.681

Then I also decided to find an optimal classifier, by using the MATLAB Classification Learner App to go over a wide range of configurations. The winning classifier was a quadratic SVM trained using 1vsALL method, and made a decent improvement. The selection process is shown below, followed by the final results.

Scene classification results visualization

Accuracy (mean of diagonal of confusion matrix) is 0.728

Category name	Accuracy	Sample training images		Sample true positives		False positives with true label		False negatives with wrong predicted label
Kitchen	0.700					Industrial	LivingRoom	Office	Industrial
Store	0.670					Industrial	LivingRoom	Industrial	Street
Bedroom	0.540					Kitchen	LivingRoom	Mountain	Kitchen
LivingRoom	0.360					Store	Bedroom	Store	Kitchen
Office	0.900					LivingRoom	Kitchen	Kitchen	Bedroom
Industrial	0.550					TallBuilding	Kitchen	LivingRoom	Suburb
Suburb	0.960					Industrial	InsideCity	InsideCity	Store
InsideCity	0.630					Industrial	Suburb	Street	Bedroom
TallBuilding	0.800					Industrial	Street	Kitchen	Industrial
Street	0.740					Bedroom	Store	Highway	Highway
Highway	0.810					Industrial	Bedroom	LivingRoom	OpenCountry
OpenCountry	0.600					Mountain	Mountain	Highway	Mountain
Coast	0.830					Highway	OpenCountry	Industrial	OpenCountry
Mountain	0.840					Industrial	Store	Highway	OpenCountry
Forest	0.990					TallBuilding	Street	Mountain
Category name	Accuracy	Sample training images		Sample true positives		False positives with true label		False negatives with wrong predicted label