|Title||Performance evaluation of visual features for structuring of video sequences and content-based image retrieval|
|Tutor||Dipl.-Ing. Martin Haller|
|Professor||Dr.-Ing. Thomas Sikora|
|Abstract||The thesis considers the performance evaluation of 44 visual features, 27 distance measures and two methods for the combination of features and distances for the cluster analysis of extracted keyframes from videos as well as for content-based image retrieval. For the evaluation of the cluster analysis, a metric Variation of Information (VI) has been considered. Content-based image retrieval has been evaluated according to measures Average Normalized Modified Retrieval Rank (ANMRR) and Mean Average Precision (MAP).|
Improvements in clustering results as well as in retrieval results can be achieved by fusing features. The results can be improved by the combination of visual features. For that a supervised sequential forward selection of features is used.
Another way to improve the results is the weighted combination of distances of different visual features. This thesis deals with the approaches for the automatic determination of these weights. Therefore, several objective measures from graph theory have been examined to what extent they are suitable for an unsupervised evaluation of clustering results.
|Key words||clustering, MPEG-7, cluster analysis, distance, evaluation, digital imaging, content analysis, CBIR|