SQL: Superpixels via quaternary labeling. (August 2019)

• Record Type: Journal Article
• Authors: Chai, Dengfeng
• Is Part Of: Pattern recognition. Volume 92(2019:Aug.)
• Page Start: 52
• Page End: 63
• Publication Date: 2019-08
• Subjects: Superpixels -- Segmentation -- Seaming -- Pixel labeling -- Graph cuts; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.03.012
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper proposes SQL algorithm to generate superpixel lattice. The number of labels is reduced from the number of superpixels to 4, and this small number assures efficient labeling. Extensive experiments demonstrate that SQL outperforms the other superpixel lattice methods and is competitive with state-of-the-art methods without lattice guarantee. SQL facilitates either MRFs or CNN to be build upon superpixels in the subsequent image analysis tasks. Abstract: This paper formulates superpixel segmentation as a pixel labeling problem and proposes a quaternary labeling algorithm to generate superpixel lattice. It is achieved by seaming overlapped patches regularly placed on the image plane. Patch seaming is formulated as a pixel labeling problem, where each label indexes one patch. Once the optimal seaming is completed, all pixels covered by one retained patch constitute one superpixel. Further, four kinds of patches are distinguished and assembled into four layers correspondingly, and the patch indexes are mapped to the quaternary layer indexes. It significantly reduces the number of labels and greatly improves labelling efficiency. Furthermore, an objective function is developed to achieve optimal segmentation. Lattice structure is guaranteed by fixing patch centers to be superpixel centers, compact superpixels are assured by horizontal and vertical constraints enforced on the smooth terms, and coherent superpixels are achieved by iteratively refining the data terms. Extensive experiments on BSDS data set demonstrate that SQL algorithm significantly improves labeling efficiency, outperforms the other superpixel lattice methods, and is competitive with state-of-the-art methods without lattice guarantee. Superpixel lattice allows contextual relationships among superpixels to be easily modeled by either MRFs or CNN.