TAILIEUCHUNG - Visual Event Recognition in Videos by Learning from Web Data

Assumptions. We assume that the logs faithfully capture events and their causality in the system’s execution. For instance, if the log declares that event X happened before event Y, we assume that is indeed the case, as the system executes. We assume that the logs record each event’s timestamp with integrity, and as close in time (as possi- ble) to when the event actually occurred in the sequence of the system’s execution. Again, we recognize that, in practice, the preemption of the system’s execution might cause a delay in the occurrence of an event X and the cor- responding log message (and timestamp generation) for entry into the log. We do. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE VOL. 34 NO. 9 SEPTEMBER 2012 1667 Visual Event Recognition in Videos by Learning from Web Data Lixin Duan Dong Xu Member IEEE Ivor Wai-Hung Tsang and Jiebo Luo Fellow IEEE Abstract We propose a visual event recognition framework for consumer videos by leveraging a large amount of loosely labeled web videos . from YouTube . Observing that consumer videos generally contain large intraclass variations within the same type of events we first propose a new method called Aligned Space-Time Pyramid Matching ASTPM to measure the distance between any two video clips. Second we propose a new transfer learning method referred to as Adaptive Multiple Kernel Learning A-MKL in order to 1 fuse the information from multiple pyramid levels and features . space-time features and static SIFT features and 2 cope with the considerable variation in feature distributions between videos from two domains . web video domain and consumer video domain . For each pyramid level and each type of local features we first train a set of SVM classifiers based on the combined training set from two domains by using multiple base kernels from different kernel types and parameters which are then fused with equal weights to obtain a prelearned average classifier. In A-MKL for each event class we learn an adapted target classifier based on multiple base kernels and the prelearned average classifiers from this event class or all the event classes by minimizing both the structural risk functional and the mismatch between data distributions of two domains. Extensive experiments demonstrate the effectiveness of our proposed framework that requires only a small number of labeled consumer videos by leveraging web data. We also conduct an in-depth investigation on various aspects of the proposed method A-MKL such as the analysis on the combination coefficients on the prelearned classifiers the convergence of the learning algorithm and the .

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