Assessing the best integration between distance-function and image-feature to answer similarity queries
Pedro H. Bugatti, Agma J. M. Traina, Caetano Traina · 2008
The retrieval of multimedia data relies on a feature extractor to provide the intrinsic characteristics (features) from the data, and a measure to quantify the similarity between them. A challenge in multimedia database systems is how to best integrate these two key aspects in order to improve the quality of the retrieved selection when answering similarity queries. In this paper, we analyze and compare a set of distance functions and feature extractors with regard to the association and dependencies among them. The results show that the most widely used and well-known distance functions, such as the Euclidean distance, do not reach a desirable similarity assessment, and reveal that a careful choice of a distance function considerably improves the retrieval of multimedia data, which in our experiments reached up to 92%.