Visual-hint Boundary to Segment Algorithm for Image Segmentation

TL;DR

The paper introduces VHBS, a new image segmentation algorithm inspired by human visual perception, which emphasizes global boundaries and color/texture differences, achieving better performance and efficiency.

Contribution

A novel segmentation method based on visual hints that aligns more closely with human perception than traditional algorithms.

Findings

VHBS outperforms traditional methods in accuracy.

VHBS maintains higher computational efficiency.

Experiments validate the effectiveness of visual hint rules.

Abstract

Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual intuition does not always follow this pattern. A new image segmentation method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is more consistent with human perceptions. VHBS abides by two visual hint rules based on human perceptions: (i) the global scale boundaries tend to be the real boundaries of the objects; (ii) two adjacent regions with quite different colors or textures tend to result in the real boundaries between them. It has been demonstrated by experiments that, compared with traditional image segmentation method, VHBS has better performance and also preserves higher computational efficiency.