Whereas many approaches, that use convolutional neural networks, are designed assuming all training data is available at training time, in many real-life scenarios this is not the case. Examples of this are web search or facial recognition. They cannot use a fixed model because the number of categories (or objects) keeps growing or changing. This type of learning is called on-line learning. The data becomes available over time and the system learns gradually. The more restricted version of this is called incremental learning. These types of learning have their challenges. For example, it has to be able to create a reliable model at each time step and it does not know in advance what the data will look like in the future or how it changes during the training.

Continue reading...

A lot of research has been done in the field of image classification. Image classification is the field of machine learning where the computer determines what the object or objects in the image are. There is a wide variety machine learning methods designed. They can loosely be divided in ‘old’- and ‘new’-school methods. There are not many papers comparing the developed methods. The developers of a methods usually compare their method with others, but are these comparisons unbiased?

In this work, we will compare different configurations of the AlexNet convolutional neural network and compare one configuration of the AlexNet with a Fisher Vector neural network. The different configurations of the CNN consist out of three different feature pooling methods, average, maximum and stochastic pooling and four different data augmentation combinations, no, crop, flip and crop and flip augmentation.

Continue reading...

Almost everyone has a smartphone these days. All these phones have a GPS and that can be used to record a track you walk, run or bicycle. In this thesis we used that data recorded by an external application and imported it into our application. The project has three parts. We need to store the data somewhere first before using it. So the first part is designing a database. The resulting database is a compact database that can run on a smartphone. Continue reading...