NDLI: Multi-label Object Categorization Using Histograms of Global Relations

Content Provider	IEEE Xplore Digital Library
Author	Mustafa, W. Hanchen Xiong Kraft, D. Szedmak, S. Piater, J. Kruger, N.
Copyright Year	2015
Description	Author affiliation: Inst. of Comput. Sci., Univ. of Innsbruck, Innsbruck, Austria (Hanchen Xiong; Szedmak, S.; Piater, J.) \|\| Maersk Mc-Kinney Moller Inst. Univ. of Southern Denmark, Odense, Denmark (Mustafa, W.; Kraft, D.; Kruger, N.)
Abstract	In this paper, we present an object categorization system capable of assigning multiple and related categories for novel objects using multi-label learning. In this system, objects are described using global geometric relations of 3D features. We propose using the Joint SVM method for learning and we investigate the extraction of hierarchical clusters as a higher-level description of objects to assist the learning. We make comparisons with other multi-label learning approaches as well as single-label approaches (including a state-of-the-art methods using different object descriptors). The experiments are carried out on a dataset of 100 objects belonging to 13 visual and action-related categories. The results indicate that multi-label methods are able to identify the relation between the dependent categories and hence perform categorization accordingly. It is also found that extracting hierarchical clusters does not lead to gain in the system's performance. The results also show that using histograms of global relations to describe objects leads to fast learning in terms of the number of samples required for training.
Starting Page	309
Ending Page	317
File Size	385367
Page Count	9
File Format	PDF
e-ISBN	9781467383325
DOI	10.1109/3DV.2015.42
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines 3D features Histograms Visualization Object affordances Three-dimensional displays Structured learning Multi-label learning Feature extraction Encoding Object categorization Joints
Content Type	Text
Resource Type	Article

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