Abstract
Cooking is a human activity with sophisticated process. Underlying the multitude of culinary recipes, there exist a set of fundamental and general cooking techniques, such as cutting, braising, slicing, and sauntering, etc. These skills are hard to learn through cooking recipes, which only provide textual instructions about certain dishs. Although visual instructions such as videos are more direct and intuitive for user to learn these skills, they mainly focus on certain dishes but not general cooking techniques. In this paper, we explore how to leverage YouTube video collections as a source to automatically mine videos of basic cooking techniques. The proposed approach first collects a group of videos by searching YouTube, and then leverages the trajectory bag of words model to represent human motion. Furthermore, the approach clusters the candidate shots into motion similar groups, and selects the most representative cluster and shots of the cooking technique to present to the user. The testing on 22 cooking techniques shows the feasibility of our proposed framework.
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Li, G., Hong, R., Zheng, YT., Yan, S., Chua, TS. (2010). Learning Cooking Techniques from YouTube. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_74
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DOI: https://doi.org/10.1007/978-3-642-11301-7_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11300-0
Online ISBN: 978-3-642-11301-7
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