An article whose abstract is: Using classifier based relevance feedback for improving image search performance

An approach for image retrieval using a very large number of highly selective features and efficient learning of queries. Approach predicated on the assumption that each image is generated by a sparse set of visual images; and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure At query time a user selects a few example images, and the AdaBoost algorithm is used to learn a classification function which depends on a small number of the most appropriate features.

Images are processed to determine a plurality of simple feature descriptors based upon characteristics of the image itself. The simple feature descriptors are grouped into complex features based upon the orientation of the simple feature descriptors. End-stopped complex feature descriptors and complex feature descriptors at multiple orientations are grouped into hypercomplex feature descriptors. Hypercomplex resonant feature descriptor clusters are generated by linking pairs of hypercomplex feature descriptors.

A system that can analyze a multi-dimensional input thereafter establishing a search query based upon extracted features from the input. In a particular example, an image can be used as an input to a search mechanism. Pattern recognition and image analysis can be applied to the image thereafter establishing a search query that corresponds to features extracted from the image input. The system can also facilitate indexing multi-dimensional searchable items thereby making them available to be retrieved as results to a search query.