Recuperação de imagens multiescala intervalar / Image retrieval by interval multiscale
AUTOR(ES)
Carlos Elias Arminio Zampieri
DATA DE PUBLICAÇÃO
2010
RESUMO
We present a general method for content-based image retrieval (CBIR) in large image collections, using multiscale interval distance estimation. We consider specifically queries by example, where the goal is to find the image in the collection that is closest to a given image, according to some image distance function. In this work we do not aim to develop metrics that best meet the user s intentions; instead, assuming that the metric is chosen, we describe an algorithm (wich we call MuSIS, for MultiScale Image Search) to perform the search efficiently using interval arithmetic. Interval estimates of the image distances are used to quickly discard candidate images after examining only small versions of them, in a manner similar to the branch-and-bound optimization paradigm. As part of this work, we developed effective interval estimators for the Euclidean distance and for some variations of it, including metrics that are sensitive to the gradient at various scales. Experiments indicate that the method yields significant cost savings over exhaustive search. Although less efficient than other methods commonly used for CBIR, the MuSIS algorithm always returns the exact answer - that is, the nearest image in metric chosen - and not just an approximation thereof. The MuSIS approach is compatible with a wide variety of distance functions without the need to pre-compute or store specific descriptors for each function
ASSUNTO(S)
gradiente normalizado interval analysis image database imagens - recuperação aritmética - intervalar normalized gradient distância euclidiana imagens - banco de dados análise de intervalos (matemática) image retrieval interval arithmetic euclidean distance
ACESSO AO ARTIGO
http://libdigi.unicamp.br/document/?code=000777438Documentos Relacionados
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