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Evaluating Temporal Information for Social Image Annotation and Retrieval
"... Abstract. Can we use the temporal evolution of annotations in Web images to improve tasks such as annotation, indexing and retrieval? This important question is the main motivation for this work. Typically visual content, text and metadata, are used to improve these tasks. A characteristic that has ..."
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Abstract. Can we use the temporal evolution of annotations in Web images to improve tasks such as annotation, indexing and retrieval? This important question is the main motivation for this work. Typically visual content, text and metadata, are used to improve these tasks. A characteristic that has received less attention, so far, is the temporal aspect of social media production and tagging. The main contribution of this paper is a thorough analysis of the temporal aspects of two popular datasets commonly used for tasks such as tag ranking, tag suggestion and tag refinement, namely NUS-WIDE and MIR-Flickr-1M. The correlation of the time series of the tags with Google searches shows that for certain concepts web information sources may be beneficial to annotate social media.
Personalized Economy of Images in Social Forums: An Analysis on Supply, Consumption, and Saliency
"... Abstract—In this work, we focus on the novel problem of analyzing individual user’s behavioral patterns regarding images shared on social forums. In particular, we view diverse user activities on social multimedia services as an economy, where the first activity mode of sharing or posting is interpr ..."
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Abstract—In this work, we focus on the novel problem of analyzing individual user’s behavioral patterns regarding images shared on social forums. In particular, we view diverse user activities on social multimedia services as an economy, where the first activity mode of sharing or posting is interpreted as supply, and another mode of activity such as commenting on images is interpreted as consumption. To characterize user profiles in these two behavioral modes, we propose an approach to characterize users ’ supply and consumption profiles based on the image content types with which they engage. We then present various statistical analyses, which confirm that there is an unexpected significant difference between these two behavioral modes. In addition, we introduce a statistical approach to identify users with salient profiles, which can be useful for social multimedia services for blocking users with undesirable behavior or viral content promotion. We showcase the benefits of the proposed saliency detection approach and its extension to detect significant key images from a complex dataset, which exhibits the inherent multi-modal nature of user bases of social multimedia services. I.
