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  1. Proceedings of the first SIGMM workshop on Social media (WSM '09)
  2. Effective semantic classification of consumer events for automatic content management
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Influence of multimedia in emerging social web systems
Tweet the debates: understanding community annotation of uncollected sources
Effective semantic classification of consumer events for automatic content management
Accelerating YouTube with video correlation
Motivating contributors in social media networks
Event driven summarization for web videos
Multimodal video copy detection applied to social media
Image tag clarity: in search of visual-representative tags for social images
The role of tags and image aesthetics in social image search
From usage to annotation: analysis of personal photo albums for semantic photo understanding
Social reader: following social networks in the wilds of the blogosphere
Implicit emotional tagging of multimedia using EEG signals and brain computer interface
VisuaPedia: a social media based visual encyclopedia

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Effective semantic classification of consumer events for automatic content management

Content Provider ACM Digital Library
Author Jiang, Wei Loui, Alexander C.
Abstract We study semantic event classification in the consumer domain by incorporating cross-domain and within-domain learning. An event is defined as a set of photos and/or videos that are taken within a common period of time, and have similar visual appearance. Events are generated from unconstrained consumer photo and video collections, by an automatic content management system, e.g., an automatic albuming system. Such consumer events have the following characteristics: an event can contain both photos and videos; there usually exist noisy/erroneous images resulting from imperfect albuming; and event data taken by different users, although from the same semantic category, can have highly diverse visual content. To accommodate these characteristics, we develop a general two-step Event-Level Feature (ELF) learning framework that enables the use of external data sources by cross-domain learning and the use of region-level representations, to enhance classification. Specifically, in the first step an elementary-level feature is used to represent images and videos. Then in the second step an ELF is constructed on top of the elementary feature to model each event as a feature vector. Semantic event classifiers can be directly built based on the ELF. Various ELFs are generated from different types of elementary-level features by using both cross-domain and within-domain learning: cross-domain approaches use two sets of concept scores at both image and region level that are learned from two external data sources; within-domain approaches use low-level visual features at both image and region level. Different types of ELFs complement each other for improved classification. Experiments over a large real consumer data set confirm significant improvements, e.g., over 90% MAP gain compared to the previous semantic event classification method.
Starting Page 35
Ending Page 42
Page Count 8
File Format PDF
ISBN 9781605587592
DOI 10.1145/1631144.1631153
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-10-23
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Cross-domain learning Concept space Region-level representation Event-level feature Semantic event classification
Content Type Text
Resource Type Article
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