QBlC* lets users find pictorial information in large image and video databases based on color, shape, texture, and sketches. QBIC technology is part of several I B M products. 'To run an interacnve query, vult the QBIC Web sewer at http //imwqbic almaden ibm COW Semantic versus nonsemantic information icture yourself as a fashion designer needing images of fabrics with a particular mixture of colors, a museum cataloger looking P for artifacts of a particular shape and textured pattern, or a movie producer needing a video clip of a red car-like object moving from right to left with the camera zooming. How do you find these images? Even though today's technology enables us to acquire, manipulate, transmit, and store vast on-line image and video collections, the search methodologies used to find pictorial information are still limited due to difficult research problems (see "Semantic versus nonsemantic" sidebar). Typically, these methodologies depend on file IDS, keywords, or text associated with the images. And, although powerful, they don't allow queries based directly on the visual properties of the images, are dependent on the particular vocabulary used, and don't provide queries for images similar to a given image. Research on ways to extend and improve query methods for image databases is widespread, and results have been presented in workshops, conferences,'.* and surveys. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns,

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