Title :
Blobworld: image segmentation using expectation-maximization and its application to image querying
Author :
Carson, Chad ; Belongie, Serge ; Greenspan, Hayit ; Malik, Jitendra
Author_Institution :
Div. of Comput. Sci., California Univ., Berkeley, CA, USA
fDate :
8/1/2002 12:00:00 AM
Abstract :
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.
Keywords :
content-based retrieval; image colour analysis; image representation; image retrieval; image segmentation; image texture; optimisation; pattern clustering; relevance feedback; Blobworld; color histograms; colour coherence; expectation maximization; fully automatic algorithm; grouping; image collections; image content; image querying; image regions; image representation; image retrieval; image segmentation; image texture coherence; joint color-texture-position feature space; knobs; object-level querying; pixel clustering; precision; raw pixel data; similarity metrics adjustment; texture histograms; Clustering algorithms; Histograms; Image databases; Image recognition; Image representation; Image retrieval; Image segmentation; Information retrieval; Pixel; Web sites;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2002.1023800