In this project, two types image representations: tiny images and bag of SIFTs, and two types of classifiers: nearest neighbor and SVM, are used to classify the scene.
In this image representation, the original image is resized to 16x16, and normalized with zero mean and unit length.
In this image representation, the original image is first smoothed using vl_imsmooth. The bin size is set to 8, the manification coefficient is set to 3, step size for building the vocabulary is 10 and the step size for getting the actual bag of SIFT features is 8, and the 'fast' option is used to reduce the running time to 300 seconds. In order to increase the accuracy, instead of increment the histogram for the minimum of distances, five entries of the histogram representing five of the smallest distances are increased by one.
K-nearest neighbor classifies the instance according to its k nearest neighbors. K is chosen to be five here.
The SVM used is a linear SVM with a lambda of 0.001. In theory, increasing the regularization will increase the training error but decrease the test error. But choosing a value of lambda that's reasonable will balance the trade off.
When using tiny images and nearest neighbor classifier, the Accuracy (mean of diagonal of confusion matrix) is 0.215
When using bag of SIFTs and nearest neighbor classifier, the Accuracy (mean of diagonal of confusion matrix) is 0.499
When using bag of SIFTs and SVM classifier, the Accuracy (mean of diagonal of confusion matrix) is 0.604
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label | ||||
---|---|---|---|---|---|---|---|---|---|
Kitchen | 0.430 | Bedroom |
LivingRoom |
Store |
InsideCity |
||||
Store | 0.350 | Kitchen |
LivingRoom |
LivingRoom |
Office |
||||
Bedroom | 0.420 | LivingRoom |
Store |
Kitchen |
Suburb |
||||
LivingRoom | 0.260 | Bedroom |
Kitchen |
Bedroom |
Kitchen |
||||
Office | 0.650 | Kitchen |
Bedroom |
Kitchen |
LivingRoom |
||||
Industrial | 0.290 | LivingRoom |
Street |
LivingRoom |
Office |
||||
Suburb | 0.830 | InsideCity |
Mountain |
Bedroom |
Forest |
||||
InsideCity | 0.590 | Industrial |
Street |
TallBuilding |
Bedroom |
||||
TallBuilding | 0.800 | Industrial |
Kitchen |
Forest |
Forest |
||||
Street | 0.750 | Mountain |
LivingRoom |
Highway |
Industrial |
||||
Highway | 0.710 | Mountain |
Suburb |
Coast |
Coast |
||||
OpenCountry | 0.480 | TallBuilding |
Street |
Coast |
Coast |
||||
Coast | 0.800 | Highway |
OpenCountry |
Highway |
OpenCountry |
||||
Mountain | 0.750 | Forest |
OpenCountry |
OpenCountry |
Highway |
||||
Forest | 0.950 | Bedroom |
TallBuilding |
OpenCountry |
Street |
||||
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label |