Graph-based Image Segmentation Using Weighted Color Patch

Xiaofang Wang (1) , Chao Zhu (1) , Charles-Edmond Bichot (1) , Simon Masnou (2)

1 imagine - Extraction de Caractéristiques et Identification
2 ICJ - Institut Camille Jordan

Constructing a discriminative affinity graph plays an essential role in graph-based image segmentation, and feature directly influences the discriminative power of the affinity graph. In this paper, we propose a new method based on the weighted color patch to compute the weight of edges in an affinity graph. The proposed method intends to incorporate both color and neighborhood information by representing pixels with color patches. Furthermore, we assign both local and global weights adaptively for each pixel in a patch in order to alleviate the over-smooth effect of using patches. The normalized cut (NCut) algorithm is then applied on the resulting affinity graph to find partitions. We evaluate the proposed method on the Prague color texture image benchmark and the Berkeley image segmentation database. The extensive experiments show that our method is competitive compared to the other standard methods with multiple evaluation metrics.

Mots clés: normalized cuts; Image segmentation; weighted color patch; affinity graph; normalized cuts.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Xiaofang Wang, Chao Zhu, Charles-Edmond Bichot, Simon Masnou. Graph-based Image Segmentation Using Weighted Color Patch. IEEE International Conference on Image Processing (ICIP), Sep 2013, Melbourne, Australia. pp.4064-4068. ⟨hal-00833305⟩