Using semantic context for multiple concepts detection in still images

Multimedia documents indexing systems performances have been improved significantly in recent years, especially after the involvement of deep learning approaches. However, this progress remains insufficient with the evolution of users' needs that become complex in terms of semantics and the number of words that compose their queries. So, it is important to think about indexing images by a group of concepts simultaneously (multi-concepts) and not just single ones. This would allow systems to better respond to queries composed of several terms. This task is much more difficult than indexing images by single concepts. Multi-concepts detection in images has been little dealt in the state of the art compared to the detection of visual single concepts. On the other hand, the use of context has proved its effectiveness in the field of multimedia semantic indexing. In this work, we propose two approaches that consider the semantic context for multi-concepts detection in still images. We tested and evaluated our proposal on the international standard corpus Pascal VOC for the detection of concepts pairs and triplets of concepts. Our contributions have shown that context is useful and improves multi-concepts detection in images. The combination of the use of semantic context and deep learning-based features yielded much better results than those of the state of the art. This difference in performance is estimated by a relative gain on mean average precision reaching + 70% for concepts pairs and + 34% for the case of triplets of concepts.