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The proliferation of wireless handheld devices is placing the World Wide Web in the palms of users, but this convenience comes at a high interactive cost. The Web that came of age on the desktop is ill-suited for use on the small displays of handhelds. Today, handheld browsing often feels like browsing on a PC with a shrunken desktop. Overreliance on scrolling is a big problem in current handheld browsing. Users confined to viewing a small portion of each page often lack a sense of the overall context --- they may feel lost in a large page and be forced to remember the locations of items as those items scroll out of view. In this paper, we present a synthesis of interaction techniques to address these problems. We implemented these techniques in a prototype, WebThumb, that can browse the live Web.

Thumbnail images provide users of image retrieval and browsing systems with a method for quickly scanning large numbers of images. Recognizing the objects in an image is important in many retrieval tasks, but thumbnails generated by shrinking the original image often render objects illegible. We study the ability of computer vision systems to detect key components of images so that automated cropping, prior to shrinking, can render objects more recognizable. We evaluate automatic cropping techniques 1) based on a general method that detects salient portions of images, and 2) based on automatic face detection. Our user study shows that these methods result in small thumbnails that are substantially more recognizable and easier to find in the context of visual search.