Topic Modeling Based Image Clustering by Events in Social Media

Bin Xu,Guoliang Fan,Dan Yang

doi:10.1155/2016/5283471

Bin Xu, Guoliang Fan + Show 1 more

Open Access

https://doi.org/10.1155/2016/5283471

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Abstract

Social event detection in large photo collections is very challenging and multimodal clustering is an effective methodology to deal with the problem. Geographic information is important in event detection. This paper proposed a topic model based approach to estimate the missing geographic information for photos. The approach utilizes a supervised multimodal topic model to estimate the joint distribution of time, geographic, content, and attached textual information. Then we annotate the missing geographic photos with a predicted geographic coordinate. Experimental results indicate that the clustering performance improved by annotated geographic information.

Highlights

Social events are events that are planned by people, attended by people, and for which the social multimedia are captured by people [1]
Experimental results show that the multimodal topic modeling combined with geographic information and image tags is helpful for the task of social media image clustering by events
We utilized the Supervised Document Neural Autoregressive Distribution Estimator (DocNADE) model to estimate the joint distribution of time information, geographic information, visual content information, and textual information

Summary

Introduction

Social events are events that are planned by people, attended by people, and for which the social multimedia are captured by people [1]. Taking Boston Marathon bombings as an example, a large number of photos taken by the crowd and shared to others during Boston Marathon Event, these photos may be a valuable clue to search for suspect. Organizing these photos automatically by event is helpful to identify the suspects. There are a lot of services provided based on location information [3] These location-based services (LBS) may be employed in a number of applications, including recommending social events in a city, locating people on a map displayed on the mobile phone, or receiving alerts. The main task of this paper is to assign missing geostamps to some photos within the collection automatically based on the existing spatiotemporal information

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