A diffusion approach for interactive image retrieval

Hassan Tabout (1) , Youssef Chahir (2) , Abderrahman Sbihi (3)

1 LASTID
2 Equipe Image - Laboratoire GREYC - UMR6072
3 UAE - Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan]

We study in this paper the problem of using multiple-instance semi-supervised learning to solve image Relevance feedback problem. Many multiple-instance learning algorithms have been proposed to tackle this problem; most of them only have a global representation of images. In this paper, we present a semi-supervised version of multiple instance learning. By taking into account both the multiple-instance and the semi-supervised properties simultaneously. A novel graph-based diffusion algorithm is developed, in which global and local information are used. Experimental results show promising results of the proposed method for a test database containing more than 2000 color seaweed images.

Mots clés: Relevance Feedback; Diffusion; Multi-Instance Learning; Seaweed images

Domaines: Traitement des images [eess.IV]

Hassan Tabout, Youssef Chahir, Abderrahman Sbihi. A diffusion approach for interactive image retrieval. Studia Informatica Universalis, 2010, 8 (4), pp.111-127. ⟨hal-00808245⟩