The goal of this project is to perform scene recognition with 3 different methods. They are:
K=1 | k=4 | k=7 | k=9 |
---|---|---|---|
20.133% | 18.933% | 18.607% | 18.333% |
K=1 | k=4 | k=7 | k=9 |
---|---|---|---|
49.067% | 48.267% | 50.000% | 50.400% |
LAMBDA=0.000001 | LAMBDA=0.00001 | LAMBDA=0.0001 | LAMBDA=0.001 |
---|---|---|---|
66.867% | 66.933% | 69.400% | 62.667% |
LAMBDA=0.01 | LAMBDA=0.1 | LAMBDA=1 | LAMBDA=10 |
---|---|---|---|
50.467% | 41.933% | 37.333% | 42.200% |
vocab=10 | vocab=20 | vocab=50 | vocab=100 | vocab=200 | vocab=400 | vocab=1000 | vocab=2000 |
---|---|---|---|---|---|---|---|
45.533% | 55.467% | 61.133% | 64.467% | 66.200% | 67.267% | 69.400% | 68.600% |
Results for bag of SIFT representation and linear SVM classifier with vocabulary size=1000, LAMBDA=0.0001
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label | ||||
---|---|---|---|---|---|---|---|---|---|
Kitchen | 0.520 | Store |
Industrial |
LivingRoom |
Office |
||||
Store | 0.570 | Bedroom |
Kitchen |
Bedroom |
Industrial |
||||
Bedroom | 0.530 | Kitchen |
Kitchen |
Industrial |
Office |
||||
LivingRoom | 0.400 | Kitchen |
Bedroom |
Bedroom |
Kitchen |
||||
Office | 0.890 | LivingRoom |
Industrial |
LivingRoom |
LivingRoom |
||||
Industrial | 0.610 | Store |
Highway |
TallBuilding |
Bedroom |
||||
Suburb | 0.960 | OpenCountry |
InsideCity |
Street |
InsideCity |
||||
InsideCity | 0.590 | Street |
Store |
Industrial |
Bedroom |
||||
TallBuilding | 0.800 | Kitchen |
Industrial |
Office |
Bedroom |
||||
Street | 0.680 | Store |
Office |
InsideCity |
Industrial |
||||
Highway | 0.820 | Mountain |
Coast |
Coast |
Coast |
||||
OpenCountry | 0.550 | Coast |
Mountain |
Coast |
Highway |
||||
Coast | 0.780 | Highway |
Highway |
OpenCountry |
OpenCountry |
||||
Mountain | 0.780 | Store |
Forest |
Coast |
OpenCountry |
||||
Forest | 0.930 | OpenCountry |
Mountain |
Mountain |
Mountain |
||||
Category name | Accuracy | Sample training images | Sample true positives | False positives with true label | False negatives with wrong predicted label |