Heterogeneous features and model selection for event-based media classification

With the rapid development of social media sites, a lot of user generated content is being shared in the Web, leading to new challenges for traditional media retrieval techniques.
An event describes the happening at a specific time and place in real-world, and it is one of the most important cues for people to recall past memories. The reminder value of
an event makes it extremely helpful in organizing human life. Thus, organizing media by events has recently drawn much attention within the multimedia research community.
In this paper, we focus on two fundamental problems related to event based social media analysis: the study of feature importance for modeling the relation between events and media,
and how to deal with missing and erroneous metadata often present in social media data. These issues are studied within an event-based media classification framework. Different
learning approaches are employed to train the event models on different features. We find, through experiments on a large set of events, that the best discriminant features are tags, spatial and temporal feature. We address the missing value problem by extending the feature with an extra attribute to indicate if the values are missing. Promising results are achieved demonstrating the effectiveness of the proposed method.