Ira Assent, Marc Wichterich, Thomas Seidl
Adaptable Distance Functions for Similarity-based Multimedia Retrieval
Abstract
Today’s abundance of storage coupled with digital technologies in virtually all scientific or commercial applications such as medical and biological imaging or music archives deal with tremendous quantities of images, videos or audio files stored in large multimedia databases. For content-based data mining and multimedia retrieval purposes, suitable similarity models are crucial. Adaptable distance functions are particularly well-suited to match the human perception of similarity. Quadratic Forms (QF) were introduced to capture the notion of interfeature similarity which sets them apart from the more traditional feature-by-feature measures from e.g. the Euclidean or Manhattan dissimilarity functions. The Earth Mover’s Distance (EMD) was adopted in Computer Vision to better approach human perceptual similarities by allowing feature transformation under a number of restrictions. After recapping the concepts of distance-based similarity search in databases, we familiarize the reader with the flexible building stones behind Quadratic Forms and the EMD. These enable their application to a large variety of multimedia retrieval problems. Unfortunately, the flexibility comes at a cost. Their computation is relatively timeconsuming, which severely limits its adoption in interactive multimedia database scenarios. Therefore, we research methods to speed up the retrieval process and show some encouraging recent results to achieve just that via an index-supported multi-step algorithm based on new lower bounding approximation techniques.
Artikel als Volltext herunterladen*
BibTeX-Eintrag für diesen Artikel
* Zum Ansehen benötigen Sie mindestens den Acrobat-Reader 5.0. Den aktuellen Acrobat-Reader bekommen Sie unter www.adobe.de/products/acrobat/
