This paper applies ideas from fractal compression and optimization theory to attack the problem of efficient content-based image indexing and retrieval. Similarity of images is measured by block matching after optimal (geometric, photometric, etc.) transformation. Such block matching which, by definition, consists of localized optimization, is further governed by a global dynamic programming technique (Viterbi algorithm) that ensures continuity and coherence of the localized block matching results. Thus, the overall optimal transformation relating two images is determined by a combination of local block-transformation operations subject to a regularization constraint. Experimental results on some limited subsets of still binary images from the mpeg-7 database demonstrate the power and potential of the proposed approach.
A fractals-inspired approach to binary image database indexing and retrieval
Annales des télécommunications, Volume 55, N°3-4, Mars-avril 2000
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in Annales des télécommunications, Volume 55, N°3-4, Mars-avril 2000 and is available at : http://dx.doi.org/10.1007/BF03001912
PERMALINK : https://www.eurecom.fr/publication/449