The tiny image representation with nearest neighbor classification can be used as a baseline to measure all subsequent classification pipelines. With tiny images, each image is represented as a scaled down version of itself (16x16 pixels). For simplicity, the aspect ratios of the original images were not maintained. Images in the test set are classified as the label for their nearest neighbor in the training set.
Accuracy (mean of diagonal of confusion matrix) is 0.225
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label | ||||
---|---|---|---|---|---|---|---|---|---|
Kitchen | 0.080 | ![]() |
![]() |
![]() |
![]() |
![]() Suburb |
![]() Bedroom |
![]() Industrial |
![]() OpenCountry |
Store | 0.020 | ![]() |
![]() |
![]() |
![]() |
![]() Street |
![]() Kitchen |
![]() OpenCountry |
![]() OpenCountry |
Bedroom | 0.180 | ![]() |
![]() |
![]() |
![]() |
![]() LivingRoom |
![]() Mountain |
![]() Kitchen |
![]() Mountain |
LivingRoom | 0.100 | ![]() |
![]() |
![]() |
![]() |
![]() Office |
![]() Street |
![]() Kitchen |
![]() Street |
Office | 0.180 | ![]() |
![]() |
![]() |
![]() |
![]() Forest |
![]() Store |
![]() TallBuilding |
![]() Coast |
Industrial | 0.130 | ![]() |
![]() |
![]() |
![]() |
![]() TallBuilding |
![]() Forest |
![]() InsideCity |
![]() Highway |
Suburb | 0.370 | ![]() |
![]() |
![]() |
![]() |
![]() Coast |
![]() Office |
![]() Industrial |
![]() OpenCountry |
InsideCity | 0.060 | ![]() |
![]() |
![]() |
![]() |
![]() Kitchen |
![]() Office |
![]() Highway |
![]() Street |
TallBuilding | 0.220 | ![]() |
![]() |
![]() |
![]() |
![]() Coast |
![]() Forest |
![]() Mountain |
![]() Mountain |
Street | 0.420 | ![]() |
![]() |
![]() |
![]() |
![]() Bedroom |
![]() InsideCity |
![]() Suburb |
![]() Suburb |
Highway | 0.560 | ![]() |
![]() |
![]() |
![]() |
![]() Coast |
![]() Office |
![]() OpenCountry |
![]() OpenCountry |
OpenCountry | 0.350 | ![]() |
![]() |
![]() |
![]() |
![]() InsideCity |
![]() Store |
![]() Coast |
![]() Highway |
Coast | 0.390 | ![]() |
![]() |
![]() |
![]() |
![]() Store |
![]() Forest |
![]() OpenCountry |
![]() Highway |
Mountain | 0.190 | ![]() |
![]() |
![]() |
![]() |
![]() InsideCity |
![]() Kitchen |
![]() Office |
![]() Bedroom |
Forest | 0.130 | ![]() |
![]() |
![]() |
![]() |
![]() OpenCountry |
![]() InsideCity |
![]() Industrial |
![]() Mountain |
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label |
With the bag of SIFT representation, images are represented as a histogram of visual words or vocabulary. This vocabulary is generated by obtaining SIFT features for the images in the training set and running K-Means against the SIFT features. The vocabulary consists of the centers of each of these K clusters. The histogram of visual words is then generated by making a soft assignment of each images SIFT features to the closest x words in the vocabulary. Afterwards the test images are run through a K-NN algorithm, as classified as the mode of the labels of their k nearest neighbors.
Accuracy (mean of diagonal of confusion matrix) is 0.556
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label | ||||
---|---|---|---|---|---|---|---|---|---|
Kitchen | 0.510 | ![]() |
![]() |
![]() |
![]() |
![]() LivingRoom |
![]() Office |
![]() Office |
![]() Bedroom |
Store | 0.450 | ![]() |
![]() |
![]() |
![]() |
![]() InsideCity |
![]() InsideCity |
![]() LivingRoom |
![]() InsideCity |
Bedroom | 0.270 | ![]() |
![]() |
![]() |
![]() |
![]() Coast |
![]() Kitchen |
![]() Kitchen |
![]() LivingRoom |
LivingRoom | 0.340 | ![]() |
![]() |
![]() |
![]() |
![]() Bedroom |
![]() Bedroom |
![]() Bedroom |
![]() Office |
Office | 0.900 | ![]() |
![]() |
![]() |
![]() |
![]() LivingRoom |
![]() Kitchen |
![]() Kitchen |
![]() LivingRoom |
Industrial | 0.240 | ![]() |
![]() |
![]() |
![]() |
![]() InsideCity |
![]() TallBuilding |
![]() InsideCity |
![]() TallBuilding |
Suburb | 0.920 | ![]() |
![]() |
![]() |
![]() |
![]() Industrial |
![]() Street |
![]() Street |
![]() LivingRoom |
InsideCity | 0.560 | ![]() |
![]() |
![]() |
![]() |
![]() Street |
![]() Highway |
![]() Street |
![]() Kitchen |
TallBuilding | 0.280 | ![]() |
![]() |
![]() |
![]() |
![]() Street |
![]() InsideCity |
![]() LivingRoom |
![]() Store |
Street | 0.610 | ![]() |
![]() |
![]() |
![]() |
![]() Industrial |
![]() Store |
![]() Store |
![]() Suburb |
Highway | 0.790 | ![]() |
![]() |
![]() |
![]() |
![]() Coast |
![]() Coast |
![]() Suburb |
![]() OpenCountry |
OpenCountry | 0.450 | ![]() |
![]() |
![]() |
![]() |
![]() Coast |
![]() Mountain |
![]() Forest |
![]() Suburb |
Coast | 0.510 | ![]() |
![]() |
![]() |
![]() |
![]() OpenCountry |
![]() OpenCountry |
![]() Bedroom |
![]() Highway |
Mountain | 0.570 | ![]() |
![]() |
![]() |
![]() |
![]() OpenCountry |
![]() Forest |
![]() OpenCountry |
![]() OpenCountry |
Forest | 0.940 | ![]() |
![]() |
![]() |
![]() |
![]() OpenCountry |
![]() Mountain |
![]() Suburb |
![]() Mountain |
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label |
The SVM classifier is creating a one vs all classifier for each category using the training set. New images are classified by running them through each SVM and choosing the one with the highest value.
Accuracy (mean of diagonal of confusion matrix) is 0.720
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label | ||||
---|---|---|---|---|---|---|---|---|---|
Kitchen | 0.630 | ![]() |
![]() |
![]() |
![]() |
![]() Store |
![]() Store |
![]() Bedroom |
![]() Bedroom |
Store | 0.650 | ![]() |
![]() |
![]() |
![]() |
![]() LivingRoom |
![]() Bedroom |
![]() LivingRoom |
![]() Highway |
Bedroom | 0.530 | ![]() |
![]() |
![]() |
![]() |
![]() LivingRoom |
![]() Kitchen |
![]() Kitchen |
![]() LivingRoom |
LivingRoom | 0.380 | ![]() |
![]() |
![]() |
![]() |
![]() Store |
![]() Industrial |
![]() Bedroom |
![]() Office |
Office | 0.890 | ![]() |
![]() |
![]() |
![]() |
![]() Kitchen |
![]() Industrial |
![]() Bedroom |
![]() Store |
Industrial | 0.590 | ![]() |
![]() |
![]() |
![]() |
![]() LivingRoom |
![]() Bedroom |
![]() Highway |
![]() InsideCity |
Suburb | 0.980 | ![]() |
![]() |
![]() |
![]() |
![]() OpenCountry |
![]() Mountain |
![]() InsideCity |
![]() TallBuilding |
InsideCity | 0.580 | ![]() |
![]() |
![]() |
![]() |
![]() Street |
![]() Suburb |
![]() Suburb |
![]() TallBuilding |
TallBuilding | 0.800 | ![]() |
![]() |
![]() |
![]() |
![]() Kitchen |
![]() InsideCity |
![]() InsideCity |
![]() InsideCity |
Street | 0.750 | ![]() |
![]() |
![]() |
![]() |
![]() Highway |
![]() Highway |
![]() InsideCity |
![]() Store |
Highway | 0.840 | ![]() |
![]() |
![]() |
![]() |
![]() OpenCountry |
![]() Store |
![]() Coast |
![]() TallBuilding |
OpenCountry | 0.580 | ![]() |
![]() |
![]() |
![]() |
![]() Coast |
![]() Coast |
![]() Highway |
![]() Highway |
Coast | 0.800 | ![]() |
![]() |
![]() |
![]() |
![]() OpenCountry |
![]() InsideCity |
![]() Highway |
![]() Bedroom |
Mountain | 0.870 | ![]() |
![]() |
![]() |
![]() |
![]() Coast |
![]() Highway |
![]() TallBuilding |
![]() OpenCountry |
Forest | 0.930 | ![]() |
![]() |
![]() |
![]() |
![]() OpenCountry |
![]() Mountain |
![]() Mountain |
![]() Suburb |
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label |