MDMKDD 2013 - The Thirteenth International Workshop on Multimedia Data Mining

The theme of this edition of the workshop is "mining largescale rich content in a networked society." Vast amount of multimedia are produced, shared, and accessed everyday in various social platforms. These multimedia objects (images, videos, texts, tags, etc.) represent rich, multifaceted recordings of human
behavior in the networked society, which lead to a range of social applications such as, (a) consumer behavior forecasting and socialdriven advertising / business, (b) local knowledge discovery (e.g., for tourism or shopping), and (c) detection of emergent news events and trends, and so on. In addition to techniques for mining single media items, all these applications require new methods for discovering robust features and stable relationships among the content of different media modalities and the users, in a dynamic, social contextrich, and likely noisy environment.
Mobile devices with multimedia sensors, such as cameras and geolocation sensors, have further integrated multimedia into people's daily life. New features, algorithms, and applications for mining the multimedia data collected at mobile devices can make these data of multiple modalities (image, video, geo, mobile data, etc.) accessible and useful in people’s daily life. Examples of such applications include (a) personal assistant, (b) augmented reality, (c) social applications, (d) entertainment, and so on. In addition to the research themes mentioned above, this workshop also welcomes submissions on various research topics of multimedia data mining, include but are not limited to the following:
● Measurement of relevance and user engagement in social media retrieval.
● Evaluation framework for the quality of rich content mining algorithms.
● Emerging technology of multimedia data mining for mobile applications.
● Emergent semantics analysis and topic detection from interrelated
multimedia objects.
● Social based business leveraging multimedia (e.g., social graph mining, social tagging, etc.).
● Data mining for location enhanced
services (e.g., maps, navigation and GIS systems).
● Fusion of multimedia features, metadata, user generated
content, and social context.
● Scalable or realtime architecture for largescale
media content processing and mining (e.g., parallel
computing, big data engineering, etc.).
● Multimedia data mining across platforms, including web and mobile devices.
● Scalable mobile multimedia computing (e.g., visual search, etc.).
● Predictive and prescriptive multimedia data modeling.
● Anomaly and outlier detection in multimedia databases.