Abstract
A new algorithm is presented which approximates the perceived visual similarity between images. The images are initially trans-formed into a feature space which captures visual structure, tex-ture and color using a tree of filters. Similarity is the inverse of the distance in this perceptual feature space. Using this algorithm we have constructed an image database system which can perform example based retrieval on large image databases. Using carefully constructed target sets, which limit variation to only a single visual characteristic, retrieval rates are quantitatively compared to those of standard methods. 1
Keywords
Image retrievalComputer scienceImage textureVisual WordArtificial intelligenceSimilarity (geometry)Pattern recognition (psychology)Feature (linguistics)Feature detection (computer vision)Feature vectorAutomatic image annotationComputer visionk-d treeImage (mathematics)VisualizationImage processingAlgorithm
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Publication Info
- Year
- 1997
- Type
- article
- Volume
- 10
- Pages
- 866-872
- Citations
- 57
- Access
- Closed
External Links
Citation Metrics
57
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Cite This
Jeremy S. De Bonet,
Paul Viola
(1997).
Structure Driven Image Database Retrieval.
, 10
, 866-872.