A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th ACM international conference on Multimedia, 2007•dl.acm.org
In this paper we introduce a 3-dimensional (3D) SIFT descriptor for video or 3D imagery
such as MRI data. We also show how this new descriptor is able to better represent the 3D
nature of video data in the application of action recognition. This paper will show how 3D
SIFT is able to outperform previously used description methods in an elegant and efficient
manner. We use a bag of words approach to represent videos, and present a method to
discover relationships between spatio-temporal words in order to better describe the video …
such as MRI data. We also show how this new descriptor is able to better represent the 3D
nature of video data in the application of action recognition. This paper will show how 3D
SIFT is able to outperform previously used description methods in an elegant and efficient
manner. We use a bag of words approach to represent videos, and present a method to
discover relationships between spatio-temporal words in order to better describe the video …
In this paper we introduce a 3-dimensional (3D) SIFT descriptor for video or 3D imagery such as MRI data. We also show how this new descriptor is able to better represent the 3D nature of video data in the application of action recognition. This paper will show how 3D SIFT is able to outperform previously used description methods in an elegant and efficient manner. We use a bag of words approach to represent videos, and present a method to discover relationships between spatio-temporal words in order to better describe the video data.
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