content-based query of image databases, inspirations from text retrieval: inverted files, frequency-based weights and relevance feedback
| Type of publication: | Inproceedings |
| Citation: | SMM1999 |
| Booktitle: | Scandinavian Conference on Image Analysis |
| Year: | 1999 |
| Pages: | 143-149 |
| Crossref: | SCIA'99 |
| 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={cbir,viper}, vgclass={refpap}, |
| Keywords: | Content-based image retrieval, image retrieval, inverted file, text retrieval |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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