Scene Summarization for Online Image Collections

Ian Simon Noah Snavely Steven M. Seitz
University of Washington

Abstract

We formulate the problem of scene summarization as selecting a set of images that efficiently represents the visual content of a given scene. The ideal summary presents the most interesting and important aspects of the scene with minimal redundancy. We propose a solution to this problem using multi-user image collections from the Internet. Our solution examines the distribution of images in the collection to select a set of canonical views to form the scene summary, using clustering techniques on visual features. The summaries we compute also lend themselves naturally to the browsing of image collections, and can be augmented by analyzing user-specified image tag data. We demonstrate the approach using a collection of images of the city of Rome, showing the ability to automatically decompose the images into separate scenes, and identify canonical views for each scene.