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LEADER 00000cam  2200505Mi 4500 
003    OCoLC 
005    20140506113917.0 
006    m     o  d         
007    cr mnu---uuaaa 
008    121227s2001    mau     o     000 0 eng   
019    SPRINGERocn852779287 
020    9781461516415|q(electronic bk.) 
020    1461516412|q(electronic bk.) 
020    |z9781461356554 
020    |z1461356555 
035    (OCoLC)852779287 
040    AU@|beng|cAU@|dOCLCO|dOCLCQ|dOCLCO|dGW5XE 
049    UMVA 
050  4 QA75.5-76.95 
072  7 UNH|2bicssc 
072  7 UND|2bicssc 
072  7 COM030000|2bisacsh 
082 04 025.04|223 
100 1  Wang, James Z. 
245 10 Integrated Region-Based Image Retrieval|h[electronic 
       resource] /|cby James Z. Wang. 
260    Boston, MA :|bSpringer US,|c2001. 
300    1 online resource (xiv, 178 pages). 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  The Information Retrieval Series,|x1387-5264 ;|v11 
520    The need for efficient content-based image retrieval has 
       increased tremendously in areas such as biomedicine, 
       military, commerce, education, and Web image 
       classification and searching. In the biomedical domain, 
       content-based image retrieval can be used in patient 
       digital libraries, clinical diagnosis, searching of 2-D 
       electrophoresis gels, and pathology slides. Integrated 
       Region-Based Image Retrieval presents a wavelet-based 
       approach for feature extraction, combined with integrated 
       region matching. An image in the database, or a portion of
       an image, is represented by a set of regions, roughly 
       corresponding to objects, which are characterized by color,
       texture, shape, and location. A measure for the overall 
       similarity between images is developed as a region-
       matching scheme that integrates properties of all the 
       regions in the images. The advantage of using this "soft 
       matching" is that it makes the metric robust to poor 
       segmentation, an important property that previous research
       has not solved. Integrated Region-Based Image Retrieval 
       demonstrates an experimental image retrieval system called
       SIMPLIcity (Semantics-sensitive Integrated Matching for 
       Picture LIbraries). This system validates these methods on
       various image databases, proving that such methods perform
       much better and much faster than existing ones. The system
       is exceptionally robust to image alterations such as 
       intensity variation, sharpness variation, intentional 
       distortions, cropping, shifting, and rotation. These 
       features are extremely important to biomedical image 
       databases since visual features in the query image are not
       exactly the same as the visual features in the images in 
       the database. Integrated Region-Based Image Retrieval is 
       an excellent reference for researchers in the fields of 
       image retrieval, multimedia, computer vision and image 
       processing. 
650  0 Computer science. 
650  0 Data structures (Computer science) 
650  0 Information storage and retrieval systems. 
650  0 Computer vision. 
650  0 Computer industry. 
655  4 Electronic books. 
776 08 |iPrint version:|z9781461356554 
830  0 Information retrieval series ;|v11. 
856 40 |3SpringerLink|uhttp://dx.doi.org.ezp.lib.unimelb.edu.au/
       10.1007/978-1-4615-1641-5|zConnect to ebook (University of
       Melbourne only) 
990    Ebook load  - do not edit, delete or attach any records. 
994    92|bUMV 
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