Project 4: Scene Recognition

Algorithm Description

For tiny images I went ahead and normalized the images by subtracting the mean and dividing by the standard deviation. For build_vocab I iterated through all the images and got sift features for each then clustered. Similary for bag_of_sift I just did a double for loop through the images, got the distances and incremented the appropiate part of the histogram for each feature. For the svm I just grouped the data based on its label, trained the relevant svm parameters and saved the result of applying the svm's to the data. Then I grabbed the svm with the highest output.

Implementation: For tiny images with knn I used a k value of ten. I got this by just testing different k values to find the best one. For the best performance of svm I had a vocab of 500 and a step size of 5 for the vocab building and the bag_of_sift step. Lambda for the best run was set to .00001. K= 10 still seemed to work the best for the knn plus bag_of_sifts model (using the same parameters as for svm), so that's what I used for the best knn plus bag_of_sifts model. Shown below are my best results. In order: tiny images with nn, svm with bag of sifts, and nn with bag of sifts. For the fast runs: I ran bag of sifts with a step size of 15 leaving the other paramters for nn and svm the same. Tiny has no change.

Results

Scene classification results visualization


Accuracy (mean of diagonal of confusion matrix) is 0.237

Category name Accuracy Sample training images Sample true positives False positives with true label False negatives with wrong predicted label
Kitchen 0.090
Store

Street

Suburb

OpenCountry
Store 0.030
Forest

InsideCity

Coast

Coast
Bedroom 0.140
Street

Kitchen

Highway

Highway
LivingRoom 0.100
Bedroom

TallBuilding

Mountain

Street
Office 0.190
Kitchen

Kitchen

Bedroom

Coast
Industrial 0.080
Store

Office

Office

Highway
Suburb 0.490
Street

Coast

Street

Street
InsideCity 0.070
Coast

Mountain

OpenCountry

LivingRoom
TallBuilding 0.180
Store

LivingRoom

Bedroom

OpenCountry
Street 0.510
Office

Store

Mountain

Suburb
Highway 0.770
Kitchen

Industrial

OpenCountry

Mountain
OpenCountry 0.370
InsideCity

Suburb

InsideCity

Suburb
Coast 0.290
TallBuilding

Bedroom

Highway

Highway
Mountain 0.160
Coast

Highway

Office

Highway
Forest 0.090
Mountain

InsideCity

Street

Bedroom
Category name Accuracy Sample training images Sample true positives False positives with true label False negatives with wrong predicted label

Scene classification results visualization


Accuracy (mean of diagonal of confusion matrix) is 0.647

Category name Accuracy Sample training images Sample true positives False positives with true label False negatives with wrong predicted label
Kitchen 0.440
Office

LivingRoom

Store

Bedroom
Store 0.580
Industrial

Industrial

Highway

InsideCity
Bedroom 0.400
Street

LivingRoom

TallBuilding

OpenCountry
LivingRoom 0.390
TallBuilding

OpenCountry

Store

Street
Office 0.830
Kitchen

Store

LivingRoom

TallBuilding
Industrial 0.560
Store

Coast

Suburb

LivingRoom
Suburb 0.940
Coast

InsideCity

OpenCountry

Industrial
InsideCity 0.480
LivingRoom

Industrial

Store

Industrial
TallBuilding 0.670
Mountain

Bedroom

Industrial

Bedroom
Street 0.690
TallBuilding

Kitchen

Highway

TallBuilding
Highway 0.810
Store

Industrial

Bedroom

LivingRoom
OpenCountry 0.540
TallBuilding

Industrial

Highway

Store
Coast 0.710
OpenCountry

OpenCountry

OpenCountry

OpenCountry
Mountain 0.740
OpenCountry

Highway

Forest

OpenCountry
Forest 0.930
Store

Store

Mountain

OpenCountry
Category name Accuracy Sample training images Sample true positives False positives with true label False negatives with wrong predicted label

Scene classification results visualization


Accuracy (mean of diagonal of confusion matrix) is 0.538

Category name Accuracy Sample training images Sample true positives False positives with true label False negatives with wrong predicted label
Kitchen 0.430
Suburb

Street

LivingRoom

InsideCity
Store 0.740
InsideCity

InsideCity

Bedroom

LivingRoom
Bedroom 0.390
Store

Suburb

LivingRoom

TallBuilding
LivingRoom 0.520
Kitchen

Kitchen

Office

Bedroom
Office 0.740
LivingRoom

Bedroom

Kitchen

Kitchen
Industrial 0.250
OpenCountry

TallBuilding

OpenCountry

Suburb
Suburb 0.840
OpenCountry

Office

Office

Street
InsideCity 0.270
Street

Street

Store

Store
TallBuilding 0.240
Bedroom

Forest

Street

Street
Street 0.590
TallBuilding

Mountain

Store

InsideCity
Highway 0.830
OpenCountry

Mountain

Store

Street
OpenCountry 0.390
Coast

Coast

Suburb

Suburb
Coast 0.380
OpenCountry

Mountain

Store

Highway
Mountain 0.510
Coast

Forest

Store

Forest
Forest 0.950
OpenCountry

OpenCountry

Suburb

TallBuilding
Category name Accuracy Sample training images Sample true positives False positives with true label False negatives with wrong predicted label

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