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  1. Proceedings of the 3rd ACM SIGMM international workshop on Social media (WSM '11)
  2. Laplacian adaptive context-based SVM for video concept detection
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Mining social media: issues and challenges
Using social media to identify events
Large-scale social media mining in facebook
Multimodal location estimation on Flickr videos
Photo stream alignment for collaborative photo collection and sharing in social media
Sentiment analysis of social media content using N-Gram graphs
IM2MAP: deriving maps from georeferenced community contributed photo collections
Near2me: an authentic and personalized social media-based recommender for travel destinations
Laplacian adaptive context-based SVM for video concept detection
Tag suggestion and localization for web videos by bipartite graph matching
Why do we converse on social media?: an analysis of intrinsic and extrinsic network factors
Chase display of social live streams (SOLISs)

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Laplacian adaptive context-based SVM for video concept detection

Content Provider ACM Digital Library
Author Jiang, Wei Loui, Alexander
Abstract Practical semantic concept detection problems usually have the following challenging conditions: the amount of unlabeled test data keeps growing and newly acquired data are incrementally added to the collection; the domain difference between newly acquired data and the original labeled training data is not negligible; and only very limited, or even no, partial annotations are available over newly acquired data. To accommodate these issues, we propose a Laplacian Adaptive Context-based SVM (LAC-SVM) algorithm that jointly uses four techniques to enhance classification: cross-domain learning that adapts previous classifiers learned from a source domain to classify new data in the target domain; semi-supervised learning that leverages information from unlabeled data to help training; multi-concept learning that uses concept relations to enhance individual concept detection; and active learning that improves the efficiency of manual annotation by actively querying users. Specifically, LAC-SVM adaptively applies concept classifiers and concept affinity relations computed from a source domain to classify data in the target domain, and at the same time, incrementally updates the classifiers and concept relations according to the target data. LAC-SVM can be conducted without newly labeled target data or with partially labeled target data, and in the second scenario the two-dimension active learning mechanism of selecting data-concept pairs is adopted. Experiments over three large-scale video sets show that LAC-SVM can achieve better detection accuracy with less computation compared with several state-of-the-art methods.
Starting Page 15
Ending Page 20
Page Count 6
File Format PDF
ISBN 9781450309899
DOI 10.1145/2072609.2072615
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-11-30
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Active annotation Cross-domain Semantic concept detection
Content Type Text
Resource Type Article
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