We present a prototype system which enables users to explore the global structure for digital imagery archives as well as drill-down into individual pictures. Our search engine builds upon computer vision advances made over the past decade in low-level feature matching, large data handling and object recognition. We demonstrate hierarchical clustering among images semi-cooperatively shot around MIT, automatic linking of flickr photos and aerial frames from the Grand Canyon, and video segment identification for a TV broadcast. Moreover, our software tools incorporate visible vs infrared band selection, color content quantization and human face detection. Ongoing and future extensions of this image search system are discussed.
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