Blobworld: A system for region-based image indexing and retrieval
Research output: Contribution to journal › Conference article › Research › peer-review
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Blobworld : A system for region-based image indexing and retrieval. / Carson, Chad; Thomas, Megan; Belongie, Serge; Hellerstein, Joseph M.; Malik, Jitendra.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1999, p. 509-517.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Blobworld
T2 - 3rd International Conference on Visual Information Systems, VISUAL 1999
AU - Carson, Chad
AU - Thomas, Megan
AU - Belongie, Serge
AU - Hellerstein, Joseph M.
AU - Malik, Jitendra
N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 1999.
PY - 1999
Y1 - 1999
N2 - Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions (“blobs”) with associated color and texture descriptors. Queryingi s based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions using a tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both querying and indexing.
AB - Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions (“blobs”) with associated color and texture descriptors. Queryingi s based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions using a tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both querying and indexing.
UR - http://www.scopus.com/inward/record.url?scp=84947441494&partnerID=8YFLogxK
U2 - 10.1007/3-540-48762-x_63
DO - 10.1007/3-540-48762-x_63
M3 - Conference article
AN - SCOPUS:84947441494
SP - 509
EP - 517
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
Y2 - 2 June 1999 through 4 June 1999
ER -
ID: 302060207