C
Cordelia Schmid
Researcher at Google
Publications - 514
Citations - 116798
Cordelia Schmid is an academic researcher from Google. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 135, co-authored 464 publications receiving 103925 citations. Previous affiliations of Cordelia Schmid include Xerox & French Institute for Research in Computer Science and Automation.
Papers
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Proceedings ArticleDOI
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Journal ArticleDOI
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
Journal ArticleDOI
Scale & Affine Invariant Interest Point Detectors
TL;DR: A comparative evaluation of different detectors is presented and it is shown that the proposed approach for detecting interest points invariant to scale and affine transformations provides better results than existing methods.
Proceedings ArticleDOI
Learning realistic human actions from movies
TL;DR: A new method for video classification that builds upon and extends several recent ideas including local space-time features,space-time pyramids and multi-channel non-linear SVMs is presented and shown to improve state-of-the-art results on the standard KTH action dataset.
Proceedings ArticleDOI
Action Recognition with Improved Trajectories
Heng Wang,Cordelia Schmid +1 more
TL;DR: Dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets are improved by taking into account camera motion to correct them.