Coloring Concept Detection in Video Using Interest Regions Coloring Concept Detection in Video Using Interest Regions Specialization: Multimedia and Intelligent Systems

Video concept detection aims to detect high-level semantic information present in video. State-of-the-art systems are based on visual features and use machine learning to build concept detectors from annotated examples. The choice of features and machine learning algorithms is of great influence on the accuracy of the concept detector. So far, intensitybased SIFT features based on interest regions have been applied with great success in image retrieval. Features based on interest regions, also known as local features, consist of an interest region detector and a region descriptor. In contrast to using intensity information only, we will extend both interest region detection and region description with color information in this thesis. We hypothesize that automated concept detection using interest region features benefits from the addition of color information. Our experiments, using the Mediamill Challenge benchmark, show that the combination of intensity features with color features improves significantly over intensity features alone.