Adaptive segmentation of traditional cultural pattern based on superpixel Log-Euclidean Gaussian metric

Abstract In order to improve the accuracy of objects similarity measurement of traditional cultural pattern segmentation and adaptively determine the number of segmentations, we propose an adaptive segmentation algorithm based on a new superpixel Log-Euclidean Gaussian metric (SLEGM) in this paper. We first propose to use the SLEGM to effectively characterize superpixels for more accurate measurement of their similarity. Because the space of Gaussians sample covariance matrix distribution is not a linear space but a Riemannian manifold, we map this manifold via matrix logarithm into a linear space, which enables us to handle Gaussians with Euclidean operations. Under the SLEGM framework, we develop an improved spectral clustering algorithm that can adaptively determine the number of clusters to achieve the adaptive segmentation for the traditional culture pattern. Extensive evaluations on the Berkeley Segmentation Data Set (BSDS500) benchmark verify that our algorithm outperforms the state-of-the-art techniques of the same category under four evaluation metrics, achieving 74.3% F-measure, 13.52% under segmentation error, 83.4% boundary recall and 97.79% achievable segmentation accuracy. Further experiments on our challenging Traditional Cultural Pattern Database (TCPD) indicate the effectiveness of our algorithm for segmenting the complex patterns.