Picture tags and world knowledge: learning tag relations from visual semantic sources
Proceedings of the 21st ACM international conference on Multimedia
Image context discovery from socially curated contents
Proceedings of the 21st ACM international conference on Multimedia
Relative spatial features for image memorability
Proceedings of the 21st ACM international conference on Multimedia
3D Wikipedia: using online text to automatically label and navigate reconstructed geometry
ACM Transactions on Graphics (TOG)
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What do people care about in an image? To drive computational visual recognition toward more human-centric outputs, we need a better understanding of how people perceive and judge the importance of content in images. In this paper, we explore how a number of factors relate to human perception of importance. Proposed factors fall into 3 broad types: 1) factors related to composition, e.g. size, location, 2) factors related to semantics, e.g. category of object or scene, and 3) contextual factors related to the likelihood of attribute-object, or object-scene pairs. We explore these factors using what people describe as a proxy for importance. Finally, we build models to predict what will be described about an image given either known image content, or image content estimated automatically by recognition systems.