Semantic Image Segmentation Based on the Global Precedence Effect and Deformable Templates

In this paper a knowledge-based automatic “object-of-interest” extraction algorithm based on the image’s partition information and deformable template matching is proposed. The proposed algorithm is based on the similarity between the template of the “object-of-interest” and a region formed by potential fusion of image segments. By simulating the “Global Precedence Effect” (forest before trees) of the human visual system (HVS), the global/large size objects are found at lower resolutions with significantly lower computational complexity. By using deformable templates, a generic template can be used for an object in different examples/ situations. 2D Deformable templates are modelled by some connected primitive regions and some application dependent flexibilities in angel, scale, etc.

Keywords:

Multiresolution image segmentation , Human visual system , Object-of-interest , Global precedence effect