PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Adding Semantics to Image-Region Annotations with the Name-It-Game
Jeroen Steggink and Cees Snoek
Multimedia Systems Volume 17, Number 5, pp. 367-378, 2011.

Abstract

In this paper we present the Name-It-Game, an interactive multimedia game fostering the swift creation of a large data set of region-based image annotations. Com- pared to existing annotation games, we consider an added semantic structure, by means of the WordNet ontology, the main innovation of the Name-It-Game. Using an ontology- powered game, instead of the more traditional annotation tools, potentially makes region-based image labeling more fun and accessible for every type of user. However, the current games often present the players with hard-to-guess objects. To prevent this from happening in the Name-It- Game, we successfully identify WordNet categories which filter out hard-to-guess objects. To verify the speed of the annotation process, we compare the online Name-It-Game with a desktop tool with similar features. Results show that the Name-It-Game outperforms this tool for semantic region-based image labeling. Lastly, we measure the accuracy of the produced segmentations and compare them with carefully created LabelMe segmentations. Judging from the quantitative and qualitative results, we believe the segmentations are competitive to those of LabelMe, espe- cially when averaged over multiple games. By adding semantics to region-based image annotations, using the Name-It-Game, we have opened up an efficient means to provide precious labels in a playful manner.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:9343
Deposited By:Christof Monz
Deposited On:16 March 2012