Stevan Rudinac
ABSTRACT
This paper presents an automatic approach that uses community-contributed images to create representative and diverse visual summaries of specific geographic areas. Complex relations between images, extracted visual features, text associated with the images as well as users and their social network are modeled using a multimodal graph. To compute affinities between nodes in the graph we rely on the proven concept of random walk with restarts. The novelty of our approach lies in its use of the multimodal graph to create a diverse, yet representative, image set. Further, we introduce an edge-weighting mechanism for the fusion of heterogeneous modalities. We evaluate our summaries with a new protocol that tests for representativeness and diversity using image geo-coordinates and is independent of the need for human evaluators. The experiments, performed on a set of Flickr images, demonstrate the effectiveness of our approach.
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Index Terms
- Finding representative and diverse community contributed images to create visual summaries of geographic areas
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