Talk by Dr. Cordelia Schmid: Learning visual human actions from movies

Dr. Cordelia Schmid is giving a talk on "Learning visual human actions from movies" within our faculty on Friday July 18 at 12:00 am at MI 00.13.009 in Garching.

Abstract of the talk

We address the problem of recognizing natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present 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. The method is shown to improve state-of-the-art results on the standard KTH action dataset by achieving 91.8% accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.

This is joint work with I. Laptev, M. Marszalek and B. Rozenfeld.

Dr. Cordelia Schmid

Cordelia Schmid holds a M.S. degree in Computer Science from the University of Karlsruhe and a Doctorate, also in Computer Science, from the Institut National Polytechnique de Grenoble (INPG). Her doctoral thesis on "Local Greyvalue Invariants for Image Matching and Retrieval" received the best thesis award from INPG in 1996. She received the Habilitation degree in 2001 for her thesis entitled "From Image Matching to Learning Visual Models". Dr. Schmid was a post-doctoral research assistant in the Robotics Research Group of Oxford University in 1996--1997. Since 1997 she has held a permanent research position at INRIA Rhone-Alpes, where she is a research director and directs the INRIA team called LEAR for LEArning and Recognition in Vision. Dr. Schmid is the author of over eighty technical publications. She has been an Associate Editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (2001--2005) and for the International Journal of Computer Vision (2004---), and she was program chair of the 2005 IEEE Conference on Computer Vision and Pattern Recognition. In 2006, she was awarded the Longuet-Higgins prize for fundamental contributions in computer vision that have withstood the test of time. She is a senior member of IEEE.