This tutorial focuses on challenges and solutions for content-based image annotation and retrieval in the context of online image sharing and tagging. We present a unified review on three closely linked problems, i.e., tag assignment, tag refinement, and tag-based image retrieval. We introduce a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. Moreover, we present an open-source testbed, with training sets of varying sizes and three test datasets, to evaluate methods of varied learning complexity. A selected set of eleven representative works have been implemented and evaluated. During the tutorial we provide a practice session for hands on experience with the methods, software and datasets. For repeatable experiments all data and code are online at http://www.micc.unifi.it/tagsurvey

Image Tag Assignment, Refinement and Retrieval / Li, Xirong; Uricchio, Tiberio; Ballan, Lamberto; Bertini, Marco; Snoek, Cees G.M.; Del Bimbo, Alberto. - ELETTRONICO. - (2015), pp. 1325-1326. (Intervento presentato al convegno ACM Multimedia 2015).

Image Tag Assignment, Refinement and Retrieval

URICCHIO, TIBERIO;BALLAN, LAMBERTO;BERTINI, MARCO;DEL BIMBO, ALBERTO
2015

Abstract

This tutorial focuses on challenges and solutions for content-based image annotation and retrieval in the context of online image sharing and tagging. We present a unified review on three closely linked problems, i.e., tag assignment, tag refinement, and tag-based image retrieval. We introduce a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. Moreover, we present an open-source testbed, with training sets of varying sizes and three test datasets, to evaluate methods of varied learning complexity. A selected set of eleven representative works have been implemented and evaluated. During the tutorial we provide a practice session for hands on experience with the methods, software and datasets. For repeatable experiments all data and code are online at http://www.micc.unifi.it/tagsurvey
2015
MM '15 Proceedings of the 23rd Annual ACM Conference on Multimedia Conference
ACM Multimedia 2015
Li, Xirong; Uricchio, Tiberio; Ballan, Lamberto; Bertini, Marco; Snoek, Cees G.M.; Del Bimbo, Alberto
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1009380
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