Analysing various methods for object extraction and the classification of kitchens

In the last few years there have been great successes in the application of deep and machine learning for the use of both object detection and classification. However, when there is a limited amount of data available for many different classes, accuracy is low and decent results can often not be obtained. This research aims to show various case-specific methods to analyse the data and to extract important features to improve classification, such as the Hough transform and mean shift segmentation. A convolution neural network, Alexnet has been trained using both the raw data and the extracted features. When training and validating the network using the raw data an accuracy of 28% has been obtained. When applying extracted features, the handles of the kitchen, to the same network accuracy improved from a 28% to an accuracy of 41%. This increase of thirteen percentage points shows that significant improvement is possible when extracting features before training a network.