Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Blobworld: a System for Region-Based Image Indexing and Retrieval (long version)

Chad Carson, Megan Thomas, Serge Belongie, Joseph M. Hellerstein and Jitendra Malik

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-99-1041
1999

http://www.eecs.berkeley.edu/Pubs/TechRpts/1999/CSD-99-1041.pdf

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. Querying is 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 good results for both querying and indexing.


BibTeX citation:

@techreport{Carson:CSD-99-1041,
    Author = {Carson, Chad and Thomas, Megan and Belongie, Serge and Hellerstein, Joseph M. and Malik, Jitendra},
    Title = {Blobworld: a System for Region-Based Image Indexing and Retrieval (long version)},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {1999},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/1999/5567.html},
    Number = {UCB/CSD-99-1041},
    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. Querying is 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 good results for both querying and indexing.}
}

EndNote citation:

%0 Report
%A Carson, Chad
%A Thomas, Megan
%A Belongie, Serge
%A Hellerstein, Joseph M.
%A Malik, Jitendra
%T Blobworld: a System for Region-Based Image Indexing and Retrieval (long version)
%I EECS Department, University of California, Berkeley
%D 1999
%@ UCB/CSD-99-1041
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/1999/5567.html
%F Carson:CSD-99-1041