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Hierarchical long-term learning for automatic image annotation

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Published in 2nd International Conference on Semantic and Digital Media Technologies, SAMT 2007 : proceedings. Genova (Italy) - Dec 5-7 - Springer. 2007, p. 28-40
Collection Lecture Notes in Computer Science; 4816
Abstract This paper introduces a hierarchical process for propagating image annotations throughout a partially labelled database. Long-term learning, where users’ query and browsing patterns are retained over multiple sessions, is used to guide the propagation of keywords onto image regions based on low-level feature distances. We demonstrate how singular value decomposition (SVD), normally used with latent semantic analysis (LSA), can be used to reconstruct a noisy image-session matrix and associate images with query concepts. These associations facilitate hierarchical filtering where image regions are matched based on shared parent concepts. A simple distance-based ranking algorithm is then used to determine keywords associated with regions.
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Research groups Computer Vision and Multimedia Laboratory
Viper group
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MORRISON, Donn Alexander, MARCHAND-MAILLET, Stéphane, BRUNO, Eric. Hierarchical long-term learning for automatic image annotation. In: 2nd International Conference on Semantic and Digital Media Technologies, SAMT 2007 : proceedings. Genova (Italy). [s.l.] : Springer, 2007. p. 28-40. (Lecture Notes in Computer Science; 4816) https://archive-ouverte.unige.ch/unige:47785

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Deposited on : 2015-03-06

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