Diploma thesis

My computer science diploma thesis "Object Classification using Local Image Features" has been written in between September 2005 and May 2006 at the Technical University of Berlin under the supervision of Marc Jäger and Professor Olaf Hellwich at the Computer Vision and Remote Sensing laboratories.

The thesis was graded 1.0 (very good, "sehr gut").


Object classification in digital images remains one of the most challenging tasks in computer vision. Advances in the last decade have produced methods to repeatably extract and describe characteristic local features in natural images. In order to apply machine learning techniques in computer vision systems, a representation based on these features is needed.

A set of local features is the most popular representation and often used in conjunction with Support Vector Machines for classification problems. In this work, we examine current approaches based on set representations and identify their shortcomings.

To overcome these shortcomings, we argue for extending the set representation into a graph representation, encoding more relevant information. Attributes associated with the edges of the graph encode the geometric relationships between individual features by making use of the meta data of each feature, such as the position, scale, orientation and shape of the feature region. At the same time all invariances provided by the original feature extraction method are retained.

To validate the novel approach, we use a standard subset of the ETH-80 classification benchmark.


thesis-nowozin-2006.pdf (1.2 Mb)

last update: Sun, 2nd July 2006