Large-Scale Semantic Concept Detection Based On Visual Contents

Indexing video by the concept is one of the most appropriate solutions for such problem. It's based on an association between a concept and its corresponding visual, sound or textual features. This kind of association is not a trivial task. It requires knowledge about the concept and its context. In this paper, we investigate a new concept detection approach to improve the performance of content-based multimedia documents retrieval systems. To achieve this goal, we tackle the problem from different plans and make four contributions at various stages of the indexing process. We first propose a new weakly supervised semi-automatic method based on the genetic algorithm to extract and annotate the video plans for training set. Subsequently, we develop a method to detect the basic concepts. We also deal with the issue of noise reduction when generating visual dictionary (BoVS). The different contributions are tested and evaluated on a big dataset (TRECVID 2015).