Quantifiable Image Nearness Approach Using Descriptive Neighbourhood
Similarity metrics plays an important role in Content-Based Image Retrieval (CBIR). This article introduces a new technique called Descriptive Proximal Coverage (DPC) to measure the quantifiable similarity index between images. This work is based on Near Neighbourhood approach in which perceptually relevant information is extracted from group of objects based on their description. Two images are considered as sets of perceptual objects and affinities between objects is defined by a tolerance relation. Two visual objects are similar if the difference between their descriptions is smaller than a tolerable level of error. Existing Notion of nearness stems from the observation that in nature it is rare to find exact objects but similar objects are often seen. It is imperative to provide a numeric value which will quantify similarity and nearness between images.
KeywordsTolerance relation Hausdorff distance Perceptual objects Granularity Near sets
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