A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation

This paper presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed -neighbor coherence segmentation criterion. More specifically, with an adaptive spatial scale and an appropriate intensity-difference scale, CCTA often achieves several sets of coherent neighboring pixels which maximize the probability of being a single image content (including kinds of complex backgrounds). In practice, each set of coherent neighboring pixels corresponds to a coherence class (CC). The fact that each CC just contains a single equivalence class (EC) ensures the separability of an arbitrary image theoretically. In addition, the resultant CCs are represented by tree-based data structures, named connected coherence tree (CCT)s. In this sense, CCTA is a graph-based image analysis algorithm, which expresses three advantages: (1) its fundamental idea, -neighbor coherence segmentation criterion, is e...

• Type: Journal
• Year: 2008
• Where: TIP
• Authors: Jundi Ding, RuNing Ma, Songcan Chen
• Keywords: -neighbor Coherence Segmentation, Coherence Segmentation Criterion, Coherent Neighboring Pixels, TIP 2008