User-Adaptive Models for Recognizing Food Preparation Activities

Stein, S. and McKenna, S. J. (2013) User-Adaptive Models for Recognizing Food Preparation Activities. In: CEA '13 Proceedings of the 5th International Workshop on Multimedia for Cooking and Eating Activities, Barcelona, Spain, 21 Oct 2013, pp. 39-44. ISBN 9781450323925 (doi: 10.1145/2506023.2506031)

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Recognizing complex activities is a challenging research problem, particularly in the presence of strong variability in the way activities are performed. Food preparation activities are prime examples, involving many different utensils and ingredients as well as high inter-person variability. Recognition models need to adapt to users in order to robustly account for differences between them. This paper presents three methods for user-adaptation: combining classifiers that were trained separately on generic and user-specific data, jointly training a single support vector machine from generic and user-specific data, and a weighted K-nearest-neighbor formulation with different probability mass assigned to generic and user-specific samples. The methods are evaluated on video and accelerometer data of people preparing mixed salads. A combination of generic and user-specific models considerably increased activity recognition accuracy and was shown to be particularly promising when data from only a limited number of training subjects was available.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Stein, Dr Sebastian
Authors: Stein, S., and McKenna, S. J.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
College/School:College of Science and Engineering > School of Computing Science

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