TY - JOUR

T1 - Blobworld

T2 - Image segmentation using expectation-maximization and its application to image querying

AU - Carson, Chad

AU - Belongie, Serge

AU - Greenspan, Hayit

AU - Malik, Jitendra

N1 - Funding Information: The authors would like to thank Ginger Ogle and Joyce Gross for their contributions to the online query system and David Forsyth, Joe Hellerstein, Ray Larson, Megan Thomas, and Robert Wilensky for useful discussions related to this work. This work was supported by a US National Science Foundation Digital Library Grant (IRI 94-11334) and by US National Science Foundation graduate fellowships for Serge Belongie and Chad Carson.

PY - 2002/8

Y1 - 2002/8

N2 - Retrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation that provides a transformation from the raw pixel data to a small set of image regions that are coherent in color and texture. This "Blobworld" representation is created by clustering pixels in a joint color-texture-position feature space. The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images. We describe a system that uses the Blobworld representation to retrieve images from this collection. An important aspect of the system is that the user is allowed to view the internal representation of the submitted image and the query results. Similar systems do not offer the user this view into the workings of the system; consequently, query results from these systems can be inexplicable, despite the availability of knobs for adjusting the similarity metrics. By finding image regions that roughly correspond to objects, we allow querying at the level of objects rather than global image properties. We present results indicating that querying for images using Blobworld produces higher precision than does querying using color and texture histograms of the entire image in cases where the image contains distinctive objects.

AB - Retrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation that provides a transformation from the raw pixel data to a small set of image regions that are coherent in color and texture. This "Blobworld" representation is created by clustering pixels in a joint color-texture-position feature space. The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images. We describe a system that uses the Blobworld representation to retrieve images from this collection. An important aspect of the system is that the user is allowed to view the internal representation of the submitted image and the query results. Similar systems do not offer the user this view into the workings of the system; consequently, query results from these systems can be inexplicable, despite the availability of knobs for adjusting the similarity metrics. By finding image regions that roughly correspond to objects, we allow querying at the level of objects rather than global image properties. We present results indicating that querying for images using Blobworld produces higher precision than does querying using color and texture histograms of the entire image in cases where the image contains distinctive objects.

KW - Clustering

KW - Expectation-Maximization

KW - Image querying

KW - Image retrieval

KW - Segmentation and grouping

UR - http://www.scopus.com/inward/record.url?scp=0036684357&partnerID=8YFLogxK

U2 - 10.1109/TPAMI.2002.1023800

DO - 10.1109/TPAMI.2002.1023800

M3 - Journal article

AN - SCOPUS:0036684357

VL - 24

SP - 1026

EP - 1038

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 8

ER -