dc.contributor.authorDuan, Lixin
dc.contributor.authorXu, Dong
dc.contributor.authorChang, Shih-Fu
dc.identifier.citationDuan, L., Xu, D., & Chang, S.-F. (2012). Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach. 2012 IEEE Conference on Computer Vision and Pattern Recognition.en_US
dc.description.abstractRecent work has demonstrated the effectiveness of domain adaptation methods for computer vision applications. In this work, we propose a new multiple source domain adaptation method called Domain Selection Machine (DSM) for event recognition in consumer videos by leveraging a large number of loosely labeled web images from different sources (e.g., Flickr.com and Photosig.com), in which there are no labeled consumer videos. Specifically, we first train a set of SVM classifiers (referred to as source classifiers) by using the SIFT features of web images from different source domains. We propose a new parametric target decision function to effectively integrate the static SIFT features from web images/video keyframes and the spacetime (ST) features from consumer videos. In order to select the most relevant source domains, we further introduce a new data-dependent regularizer into the objective of Support Vector Regression (SVR) using the ϵ-insensitive loss, which enforces the target classifier shares similar decision values on the unlabeled consumer videos with the selected source classifiers. Moreover, we develop an alternating optimization algorithm to iteratively solve the target decision function and a domain selection vector which indicates the most relevant source domains. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method DSM over the state-of-the-art by a performance gain up to 46.41%.en_US
dc.rights© 2012 IEEE.en_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titleExploiting web images for event recognition in consumer videos : a multiple source domain adaptation approachen_US
dc.typeConference Paper
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (2012 : Providence, Rhode Island, US)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US

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