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Towards a Transmedia Search Engine : A User Study on Perceiving Analogies in Multimedia Data

The World Wide Web has become a reliable and fast way to archive and share multimedia data such as music, images, and videos. Most methods to search multimedia data are text-based and rely on filenames or text tags attached to the file. Those that do not rely on text are contentbased. These search engines focus on exact matches and do not compare different media forms. Human cognition is much more complex, however. We typically use visual or phonetic comparisons which are then secondarily translated into language for further communication. Further, we often perceive similarities between different media forms and find analogies in nonliteral, inexact matches. The goal of our research is to develop a Transmedia Search Engine that suggests analogies across different media forms (e.g., audio, images, videos) by looking at structural, content-based, similarity within media content. To find the most effective algorithms to achieve this goal, we are studying how people perceive similarity between and within media forms. We describe the development and results of two surveys that we have conducted on similarity between images. The first is designed to capture people's visceral reactions on how two media samples relate, while the second delves deeper into why two media samples are perceived as similar. Our results show that subject and shape are leading factors in determining similarity and are highly correlated in that study participants tend to identify both as significant factors in their perception of images as being similar.