Suh, B.
July 2006
Ph.D. Dissertation from the Department of Computer Science
With the popular usage of personal image devices and the continued increase of computing power, casual users need to handle a large number of images on computers. Image management is challenging because in addition to searching and browsing textual metadata, we also need to address two additional challenges. First, thumbnails, which are representative forms of original images, require significant screen space to be represented meaningfully. Second, while image metadata is crucial for managing images, creating metadata for images is expensive. My research on these issues is composed of three components which address these problems. First, I explore a new way of browsing a large number of images. I redesign and implement a zoomable image browser, PhotoMesa, which is capable of showing thousands of images clustered by metadata. Combined with its simple navigation strategy, the zoomable image environment allows users to scale up the size of an image collection they can comfortably browse. Second, I examine tradeoffs of displaying thumbnails in limited screen space. While bigger thumbnails use more screen space, smaller thumbnails are hard to recognize. I introduce an automatic thumbnail cropping algorithm based on a computer vision saliency model. The cropped thumbnails keep the core informative part and remove the less informative periphery. My user study shows that users performed visual searches more than 18% faster with cropped thumbnails. Finally, I explore semi-automatic annotation techniques to help users make accurate annotations with low effort. Automatic metadata extraction is typically fast but inaccurate while manual annotation is slow but accurate. I investigate techniques to combine these two approaches. My semi-automatic annotation prototype, SAPHARI, generates image clusters with facilitate efficient bulk annotation. For automatic clustering, I present hierarchical event clustering and clothing based human recognition. Experimental results demonstrate the effectiveness of the semi-automatic annotation when applied on personal photo collections. Users were able to make annotation 49% and 6% faster with semi-automatic annotation interface on event and face tasks, respectively.
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