Sreyasi Nag Chowdhury, William Cheng, Gerard de Melo, Simon Razniewski, G. Weikum
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Human perception is known to be predominantly visual. As modern web infrastructure promoted the storage of media, the web-data paradigm shifted from text-only documents to those containing text and images. A multitude of blog posts, news articles, and social media posts exist on the Internet today as examples of multimodal stories. The manual alignment of images and text in a story is time-consuming and labor intensive. We present a web application for automatically selecting relevant images from an album and placing them in suitable contexts within a body of text. The application solves a global optimization problem that maximizes the coherence of text paragraphs and image descriptors, and allows for exploring the underlying image descriptors and similarity metrics. Experiments show that our method can align images with texts with high semantic fit, and to user satisfaction.
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