Active Learning with Multiple Views

For many real world object recognition tasks a common difficulty is the high cost of generating labels for a large pool of unlabeled images. In order to learn a concept with the help of a human expert, we aim at picking only a small subset of examples that is most "helpful" for the classifier. The concept of active learning tackles this setting by enabling a classifier to pose specific queries that are chosen from an unlabeled dataset.

To create a representation of an image that the computer understands, numerical features of an image can be calculated. These features may describe for example the shape, brightness, or texture of an object. They are usually stringed together to form a long (high-dimensional) feature vector. However, such high-dimensional feature vectors can cause problems in finding global optima for the parameter space of the classification model.

One way to overcome this problem is feature selection or feature weighting to determine the most relevant features for a classification task. Many methods have been developed but they rely on a sufficiently large labeled dataset. In many problem settings (like in this active learning setting), labeled data may not be available; therefore we try to take a different perspective: we describe objects with different views.

For example, each feature module in image classification can be seen as a view on the object. Each view can describe different concepts and each view can contribute to a certain degree to the target concept that is to be learned. Having multiple representations can improve classification performance when, in addition to labeled examples, many unlabeled examples are available.

The aim of this research project is to combine active learning and multi-view methods to derive new and more enhanced selection strategies and to improve the classification accuracy with few labeled examples. To learn more about this project, contact Nicholas Cebron.