Blobworld: image segmentation using expectation-maximization and its application to image querying
University of California, Berkeley · University of California, San Diego · +1 more institution
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…
Citation impact
- FWCI
- 57.44
- Percentile
- 100%
- References
- 55
Authors
4Topics & keywords
- Image texture
- Computer science
- Artificial intelligence
- Computer vision
- Image segmentation
- Feature detection (computer vision)
- Automatic image annotation
- Pattern recognition (psychology)