Visual Taxometric Approach to Image Segmentation Using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions

Abstract

Images convey multiple meanings that depend on the context in which the viewer perceptually organizes the scene. By assuming a standardized natural-scene-perception-taxonomy comprised of a hierarchy of nested spatial-taxons [17] [6] [5], image segmentation is operationalized into a series of two-class inferences. Each inference determines the optimal spatial-taxon region, partitioning a scene into a foreground, subject and salient objects and/or sub-objects. I demonstrate the results of a fuzzy-logic-natural-vision-processing engine that implements this novel approach. The engine uses fuzzy-logic inference to simulate low-level visual processes and a few rules of figure-ground perceptual organization. Allowed spatial-taxons must conform to a set of ”meaningfulness” cues, as specified by a generic scene-type. The engine was tested on 70 real images composed of three ”generic scene-types”, each of which required a different combination of the perceptual organization rules built into our model. Five human subjects rated image-segmentation quality on a scale from 1 to 5 (5 being the best). The majority of generic-scene-type image segmentations received a score of 4 or 5 (very good, perfect). ROC plots show that this engine performs better than normalized-cut [9] on generic-scene type images.