Salient objects detection in dynamic scenes using color and texture features

Abstract : Visual saliency is an important research topic in the field of computer vision due to its numerous possible applications. It helps to focus on regions of interest instead of processing the whole image or video data. Detecting visual saliency in still images has been widely addressed in literature with several formulations. However, visual saliency detection in videos has attracted little attention, and is a more challenging task due to additional temporal information. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In our work, we model the dynamic textures in a dynamic scene with local binary patterns to compute the dynamic saliency map, and we use color features to compute the static saliency map. Both saliency maps are computed using a bio-inspired mechanism of human visual system with a discriminant formulation known as center surround saliency, and are fused in a proper way. The proposed model has been extensively evaluated with diverse publicly available datasets which contain several videos of dynamic scenes, and comparison with state-of-the art methods shows that it achieves competitive results.
Type de document :
Article dans une revue
Multimedia Tools and Applications, Springer Verlag, 2017, pp.1-14. 〈https://link.springer.com/article/10.1007/s11042-017-4462-y〉. 〈10.1007/s11042-017-4462-y〉
Liste complète des métadonnées

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01493368
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : mardi 21 mars 2017 - 14:28:03
Dernière modification le : samedi 14 juillet 2018 - 01:06:13

Lien texte intégral

Identifiants

Collections

Citation

Satya M. Muddamsetty, Désiré Sidibé, Alain Trémeau, Fabrice Mériaudeau. Salient objects detection in dynamic scenes using color and texture features. Multimedia Tools and Applications, Springer Verlag, 2017, pp.1-14. 〈https://link.springer.com/article/10.1007/s11042-017-4462-y〉. 〈10.1007/s11042-017-4462-y〉. 〈hal-01493368〉

Partager

Métriques

Consultations de la notice

137