Retrieving images from large and varied collections using image content as a key is a challenging and important problem. In this paper, we present a new image representation which provides a transformation from the raw pixel data to a small set of localized coherent regions in color and texture space. This so-called “blobworld” representation is based on segmentation using the expectation-maximization algorithm on combined color and texture features. The texture features we use for the segmentation arise from a new approach to texture description and scale selection. We describe a system that uses the blobworld representation to retrieve images. An important and unique aspect of the system is that, in the context of similarity-based querying, 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, the outcome of many queries on these systems can be quite inexplicable, despite the availability of knobs for adjusting the similarity metric
Region-based image querying
1997-01-01
1509690 byte
Conference paper
Electronic Resource
English
British Library Conference Proceedings | 1997
|Image Classification and Querying Using Composite Region Templates
British Library Online Contents | 1999
|Saliency field map construction for region-of-interest-based color image querying
British Library Online Contents | 2005
|Content based Image Retrieval through Object Extraction and Querying
British Library Conference Proceedings | 2000
|