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An interactive machine for algae image retrieval

Hassan Tabout 1 Abdelmoghit Souissi 1 Youssef Chahir 2 Abderrahman Sbihi 3
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : 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 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.
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https://hal.archives-ouvertes.fr/hal-00815331
Contributor : Yvain Queau <>
Submitted on : Thursday, April 18, 2013 - 3:11:33 PM
Last modification on : Wednesday, November 13, 2019 - 3:41:40 PM

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Hassan Tabout, Abdelmoghit Souissi, Youssef Chahir, Abderrahman Sbihi. An interactive machine for algae image retrieval. IEEE ICCSIT 2009, Aug 2009, Beijing, China. pp.176 - 180, ⟨10.1109/ICCSIT.2009.5234597⟩. ⟨hal-00815331⟩

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