Primary‐view perception on a gray image: Region segmentation and association

Abstract

The segmentation of scenes into perceptually meaningful partitions possessing a useful relationship among them is of great importance in image understanding. In this paper, an effective approach performing image segmentation and association among the segmented regions for a gray image is presented. An unsupervised segmentation process using a self-organization map (SOM) and spatial-distance computation is presented for the segmentation of clusters in our understanding system. An association process is developed for the construction of spatial relationships among the attended regions, which are obtained from the segmented clusters using fuzzy relations. The algorithms for each process are presented and exemplified with a series of illustrations. This approach is applied to simulating the primary-view perception on a natural gray image. Experiments show that the proposed approach is feasible. A possible linguistic presentation, based on the attended regions and their relationships, is given for every test image.

Color image segmentation using a self-organizing map algorithm

“…That is, the aim is to simulate primary-view visual perception described in Ref. 36, whose approach was designed to deal with gray scale images. The regions displayed and their relationships are produced to represent the global meaning of an image, and this global meaning can be further explored by means of linguistic variables and inference rules.…”

“…When human beings look at an image, some regions are perceived as primary-view perception, 36 such as the sky, building, and grass in the image shown in Fig. 3͑a͒.…”