Abstract
This paper presents a new approach for building semantic image indexing and retrieval systems. Our approach is composed of four phases : (1) knowledge acquisition, (2) weakly-supervised learning, (3) indexing and (4) retrieval. Phase 1 is driven by a visual concept ontology which helps the expert to define low-level features useful to characterize object classes. Phase 2 uses acquired knowledge and image samples to learn the mapping between image data and visual concepts. Image indexing phase (phase 3) is fully automatic and produces semantic annotations of the images to index. The symbolic nature of querying enables user-friendly and fast retrieval (phase 4). We have applied our approach to the domain of transport vehicles (i.e. motorbikes, aircrafts, cars).
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© 2005 Springer-Verlag Berlin Heidelberg
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Maillot, N., Thonnat, M. (2005). A Weakly Supervised Approach for Semantic Image Indexing and Retrieval. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_66
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DOI: https://doi.org/10.1007/11526346_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27858-0
Online ISBN: 978-3-540-31678-7
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