The motive behind the project is to investigate techniques used in image recognition, specifically, for the problem domain of scene recognition.
The method used is building a prediction model using a training date set. We apply several feature extraction algorithms to get useful features from the training dataset, then applying classification algorithms to predict the class of the test dataset. A key to generalise the building model is subjecting it to validation. The training data-set was split into different portions used to train and validate the model.
The key result is, with simple feature extraction and classifications, prediction success rates were very limited, not higher than 20~25%. Applying more sophisticated algorithms yield in significantly higher prediction success rates to 60~65%.
The conclusion is the kind-ness of the problem space we are dealing with in the report, it is very challenging to get success ratios around 80~85%. Such ratios requires spatial feature extraction, adding additional complementary features and applying non-linear classification methods.
This project was built as a coursework for the ‘Computer Vision’, 1st semester in MSc Artificial Intelligence at the University of Southampton.