Blobworld: A System for Region-Based Image Indexing and Retrieval

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Blobworld: A System for Region-Based Image Indexing and Retrieval (1999)

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

Venue:	In Third International Conference on Visual Information Systems
Citations:	375 - 4 self

BibTeX

@INPROCEEDINGS{Carson99blobworld:a,
    author = {Chad Carson and Megan Thomas and Serge Belongie and Joseph M. Hellerstein and Jitendra Malik},
    title = {Blobworld: A System for Region-Based Image Indexing and Retrieval},
    booktitle = {In Third International Conference on Visual Information Systems},
    year = {1999},
    pages = {509--516},
    publisher = {Springer}
}

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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 encouraging results for both querying and indexing. 1 Introduction From a user's point of view, the performance of an information retrieval system can be measured by the quality and speed with which it answers the user's information need. Several factors contribute to overall performance: -- the time required to run each individual query, -- the quality (precision/recall) of each i...

Keyphrases

region-based image indexing image retrieval entire image high-dimensional feature space several factor texture descriptor encouraging result information retrieval system individual query coherent image region precision recall overall performance high-dimensional distance blob description large-scale retrieval information need lower-rank approximation

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