Storage Retrieval

Download Image databases : search and retrieval of digital imagery by Vittorio Castelli, Lawrence D. Bergman PDF

By Vittorio Castelli, Lawrence D. Bergman

The explosive development of multimedia facts transmission has generated a serious want for effective, high-capacity photograph databases, in addition to robust se's to retrieve photograph information from them. This ebook brings jointly contributions through a global all-star crew of innovators within the box who proportion their insights into all key elements of photo database and seek engine building. Readers get in-depth discussions of the whole variety of the most important photo database structure, indexing and retrieval, transmission, exhibit, and consumer interface concerns. And, utilizing examples from an array of disciplines, the authors current state of the art purposes in clinical imagery, multimedia communications, earth technology, distant sensing, and different significant program parts

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Any improvement in interactivity, while pushing toward a more efficient exploitation of human resources during the retrieval process, also proves particularly appealing for commercial applications supporting nonexpert (hence more impatient and less adaptive) users. Often a good interface can let the user express queries that go beyond the normal system representation power, giving the user the impression of working at a higher semantic level than the actual one. As an example, sky images can be effectively retrieved by a blue color sketch in the top part of the canvas; similarly, “all leopards” in an image collection can be retrieved by querying for texture (possibly invariant to scale), using a leopard’s coat as an example.

Colombo, A. Del Bimbo, and P. Pala, Semantics in visual information retrieval, IEEE Multimedia 6(3), 38–53 (1999). 23. S. Santini and R. Jain, Similarity measures, IEEE Trans. Pattern Anal. Machine Intell. 21(9), 871–883 (1999). 24. T. Minka and R. Picard, Interactive learning with a society of models, Pattern Recog. 30(4), 565–582 (1997). 25. A. Gupta, S. Santini, and R. Jain, In search of information in visual media, Commun. ACM 40(12), 35–42 (1997). 26. A. Del Bimbo and P. Pala, Image retrieval by multiple features combination, Technical Note, Department of Systems and Informatics, University of Florence, Italy, 1999.

8 shows color similarity retrieval results using a painting by Cezanne as the query image. Notice how many of the retrieved images are actually paintings by the same painter; this is sensible, as it reflects the preference of each artist for specific color combinations. Objects are annotated manually (but not textually) in each image by drawing their contour. For the purpose of shape-based retrieval, queries are submitted by sketch; query and database shapes are compared using an energy-minimization procedure, in which the sketch is elastically deformed to best fit the target shape [32].

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