Content based indexing is computed from input that consists of matching values between images and templates. The key idea is to embed both images and templates in a low-dimensional Euclidean space so that matching between embedded images and embedded templates approximates the given input. It is shown that such embedding can be computed by means of a singular value decomposition of the input matrix. Classic principal component analysis is shown to be a special case of the proposed technique, corresponding to the case where the templates and the images are the same.
Template matching approach to content based image indexing by low dimensional Euclidean embedding
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 2 ; 566-571 vol.2
2001-01-01
527149 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Template Matching Approach to Content Based Image Indexing by Low Dimensional Euclidean Embedding
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