Transfer Learning for Cross-Dataset Recognition: A Survey <s> Video Event Detection <s> Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Video Event Detection <s> Multimedia event detection (MED) has a significant impact on many applications. Though video concept annotation has received much research effort, video event detection remains largely unaddressed. Current research mainly focuses on sports and news event detection or abnormality detection in surveillance videos. Our research on this topic is capable of detecting more complicated and generic events. Moreover, the curse of reality, i.e., precisely labeled multimedia content is scarce, necessitates the study on how to attain respectable detection performance using only limited positive examples. Research addressing these two aforementioned issues is still in its infancy. In light of this, we explore Ad Hoc MED, which aims to detect complicated and generic events by using few positive examples. To the best of our knowledge, our work makes the first attempt on this topic. As the information from these few positive examples is limited, we propose to infer knowledge from other multimedia resources to facilitate event detection. Experiments are performed on real-world multimedia archives consisting of several challenging events. The results show that our approach outperforms several other detection algorithms. Most notably, our algorithm outperforms SVM by 43% and 14% comparatively in Average Precision when using Gaussian and Χ2 kernel respectively. <s> BIB002
Video event detection or video concept detection is to automatically classify video shots into certain semantic event (such as making a cake, wedding ceremony, and changing a vehicle tire) or concept (such as meeting, sports, and entertainment). We discuss the two tasks together, because they are both to extract semantic meaning from a video clip. Most commonly used data for evaluating video event detection are from TREC VideoRetrieval Evaluation (TRECVID) dataset series 34 . The transfer learning is used for cross-task video event detection by previous research. For example, the TRECVID 2005 dataset is used by BIB001 , where one of the 39 concepts is picked as the target concept and one of the 13 programs is the target program. Differently, the TRECVID 2010 and TRECVID 2011 are used together by BIB002 for the crossdomain event detection, where the TRECVID 2011 semantic indexing task (SIN11) is used as the source auxiliary dataset.