1996
DOI: 10.1007/978-1-4613-1387-8_2
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Photobook: Content-Based Manipulation of Image Databases

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Cited by 357 publications
(450 citation statements)
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“…In recent years, textural information has been widely used as a visual primitive in many image processing applications [1], [2], [3], [4]. The potential areas include industrial and biomedical surface inspection, ground classification and segmentation of satellite or aerial imagery, document analysis, scene analysis, texture synthesis for computer graphics and animation, biometric person authentication, content-based image retrieval and modelbased image coding [5], [6], [7].…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, textural information has been widely used as a visual primitive in many image processing applications [1], [2], [3], [4]. The potential areas include industrial and biomedical surface inspection, ground classification and segmentation of satellite or aerial imagery, document analysis, scene analysis, texture synthesis for computer graphics and animation, biometric person authentication, content-based image retrieval and modelbased image coding [5], [6], [7].…”
Section: Introductionmentioning
confidence: 99%
“…In semantic approach, a hierarchical image [20] classification is done. Although many visual information systems have been developed recently [4], [7], [21], none of these systems operate by considering knowledge extracted from image repositories. In this paper, first I divide the whole image databases into small number of subgroups in accordance with the image semantics i.e.…”
Section: Semanticsmentioning
confidence: 99%
“…The first technique is not applicable to the volcano detection problem, and the second is inappropriate because naturally occurring objects (such as volcanoes) possess much greater variability in appearance than rigid man-made objects. Several prototype systems (Flickner et al 1995;Pentland, Picard, and Sclaroff, 1996;Picard and Pentland, 1996) that permit querying by content have been developed in the computer vision community. In general, these systems rely on color histograms, regular textures, and boundary contours or they assume that objects are segmented and well-framed within the image.…”
Section: Related Workmentioning
confidence: 99%