Blobworld: A system for region-based image indexing and retrieval

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Standard

Blobworld : A system for region-based image indexing and retrieval. / Carson, Chad; Thomas, Megan; Belongie, Serge; Hellerstein, Joseph M.; Malik, Jitendra.

I: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1999, s. 509-517.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Carson, C, Thomas, M, Belongie, S, Hellerstein, JM & Malik, J 1999, 'Blobworld: A system for region-based image indexing and retrieval', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), s. 509-517. https://doi.org/10.1007/3-540-48762-x_63

APA

Carson, C., Thomas, M., Belongie, S., Hellerstein, J. M., & Malik, J. (1999). Blobworld: A system for region-based image indexing and retrieval. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 509-517. https://doi.org/10.1007/3-540-48762-x_63

Vancouver

Carson C, Thomas M, Belongie S, Hellerstein JM, Malik J. Blobworld: A system for region-based image indexing and retrieval. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1999;509-517. https://doi.org/10.1007/3-540-48762-x_63

Author

Carson, Chad ; Thomas, Megan ; Belongie, Serge ; Hellerstein, Joseph M. ; Malik, Jitendra. / Blobworld : A system for region-based image indexing and retrieval. I: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1999 ; s. 509-517.

Bibtex

@inproceedings{0f933b2bf45040a6b5573cf3bee6f83d,
title = "Blobworld: A system for region-based image indexing and retrieval",
abstract = "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.",
author = "Chad Carson and Megan Thomas and Serge Belongie and Hellerstein, {Joseph M.} and Jitendra Malik",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1999.; 3rd International Conference on Visual Information Systems, VISUAL 1999 ; Conference date: 02-06-1999 Through 04-06-1999",
year = "1999",
doi = "10.1007/3-540-48762-x_63",
language = "English",
pages = "509--517",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",

}

RIS

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