VISEN: A Video Interactive Retrieval Engine Based on Semantic Network in large video collections

Following technological advances carried out recently, there has been an explosion in the quantity of videos available and their accessibility. This is largely justified by the fall of the prices of acquisition and the increase of the capacity of the memory supports, which made the storage of the large document video in computer system possible. To allow an effective exploitation of the collections, it is necessary to install tools facilitating the access to the documents and handle them. In this context, we propose a multimedia retrieval approach that puts the user at the center of the retrieval process starting from a text query. The new aspects of our proposal is as follows: (i) concerning the indexation part, we propose a new approach allowing a multi-level and semantic classification of videos, (ii) regarding the retrieval part, the inclusion of query expansion mechanism helps the user to formulate the query and the relevance feedback mechanism which helps improve the results considering the user's feedback. Our contribution at the experimental level consists in the implementation of prototype VISEN. In fact the technique proposed have been integrated in system seeks by the contents to evaluate the contribution in terms of effectiveness and precision. After carrying out a set of tests on 2700 videos and 62838 images, the experimental results showed that the proposed algorithm performs well.