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This intuitive solution for image browsing provides a continuous experience of navigating through an infinite 2D grid arranged by similarity. In contrast to common multidimensional embedding methods, our solution does not entail an upfront creation of a full global map. Image map generation is dynamic, fast and scalable, independent of the number of images in the dataset, and seamlessly supports online updates to the dataset. Thus, the technique is a viable solution for massive and constantly varying datasets consisting of millions of images.
Evaluation of our approach shows that when using DynamicMaps, users viewed many more images per minute compared to a standard relevance feedback interface, suggesting that it supports more fluid and natural interaction that enables easier and faster movement in the image space. Most users preferred DynamicMaps, indicating it is more exploratory, better supports serendipitous browsing and more fun to use.
Yanir Kleiman, Joel Lanir, Dov Danon, Yasmin Felberbaum, Daniel Cohen-Or (2015). DynamicMaps: Similarity-based Browsing through a Massive Set of Images. In proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2015. pp. 995-1014. ACM Press. Download PDF
Yanir Kleiman, Joel Lanir, Noa Fish, & Daniel Cohen-Or. (2013). Dynamic Maps for Exploring and Browsing Shapes. Computer Graphics Forum, Vol. 32, No. 5, pp. 187-196. The Eurographics Association and Blackwell Publishing Ltd. Download PDF