content-based query of image databases, inspirations from text retrieval: inverted files, frequency-based weights and relevance feedback
| Type of publication: | Techreport |
| Citation: | SMM1998 |
| Number: | 98.04 |
| Year: | 1998 |
| Month: | november |
| Institution: | Computer Vision Group, Computing Centre, University of Geneva |
| Address: | rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve, Switzerland |
| Abstract: | In this paper we report the application of techniques inspired by text retrieval research to the content-based query of image databases. In particular, we show how the use of an inverted file data structure permits the use of a feature space of $\mathcal{O}(10^4)$ dimensions, by restricting search to the subspace spanned by the features present in the query. A suitably sparse set of colour and texture features is proposed. A scheme based on the frequency of occurrence of features in both individual images and in the whole collection provides a means of weighting possibly incommensurate features in a compatible manner, and naturally extends to incorporate relevance feedback queries. The use of relevance feedback is shown consistently to improve system performance, as measured by precision and recall. |
| Userfields: | vgproject={viper,cbir}, vgclass={report}, |
| Keywords: | |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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