Emulating human visual perception for measuring difference in images using an SPN graph approach

This paper presents a new methodology for efficiently representing the content of images and comparing images by detecting and recording their visual differences. In particular, the methodology presented here is based on a stochastic Petri-net (SPN) graph approach able to extract and record local and global features from both images, compare them, and define the percentage of similarity. One of the features of the human visual perception is the detection of similarities between two images. The visual similarity is based on color, size, shape, and local and global topological changes of the image regions. Several methods dealing with image or object similarities have been proposed. The new feature of the method here is the partial emulation of the human observer's visual perception by recording differences extracted from different images. Results of the method described here are presented for a variety of images by using local and global noisy conditions.