Density-based retrieval from high-similarity image databases

Many image classification problems can fruitfully be thought of as image retrieval in a "high similarity image database" (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita distances between distributions of local (pixelwise) features estimated from a set of automatically and consistently defined image regions. The weight coefficients are estimated based on optimal retrieval performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition.

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