Modelling Multimedia Indexing Algorithms Description for Implicit and Explicit Indexation
Résumé
The increasing amount of multimedia data is generally indexed by algorithms which extract metadata that will be further queried by users for retrieving some desired information. Because of the heterogeneity and the diversity of these indexing algorithms, it is not suitable to execute at once all indexing algorithms. Actually, it will overload the information system and will also produce metadata that may not be used inside the system. For example, in a car park surveillance system, it is not necessary to apply to all the video sequences algorithms that detect persons. However, at a certain time someone might want to know about the persons that crossed the parking. It can happen that the multimedia information system returns an empty result to a query because the relevant algorithm for treating the content has not been identified or located. In this case, an algorithm application chain could produce the metadata that could give an answer to the query. In order to identify the right indexing tool(s) for the right resource(s), it would be useful to have a description attached to these tools. In this paper, we propose an indexing algorithm description model and show how to use such a model for determining an appropriate set of indexing algorithms (or sequences of algorithms) according to specific user needs, properties and execution contexts. Our proposal has been integrated in the LINDO project for implicitly and explicitly indexing multimedia contents.