Segmentation of radar images by combining watershed and Fisher techniques for target classification

Belkhaoui, B.; Toumi, Abdelmalek; Khalfallah, Ali; Khenchaf, Ali; Bouhlel, Med Salim

doi:10.1109/codit.2013.6689517

Cited by 4 publications

(1 citation statement)

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“…Belkhaoui et al combined FD with watershed algorithm, a process and method for auto target recognition based on inverse synthetic aperture radar image to solve the target recognition of the radar image. 22 The principle and related work of FD will be described in detail below.…”

Section: Efdmentioning

confidence: 99%

Clustering stability evaluation method based on SSIM

et al. 2019

Journal of Algorithms & Computational Technology

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In the clustering validity analysis, three main methods including intra-class cohesion, inter-class separation, and artificial judgment index can be used to evaluate the clustering results. If the clustering result is efficient, it means that the clustering stability is better. However, when those methods are used, it is essential to provide the sample data or clustering algorithms in advance. This paper proposes a clustering stability evaluation method based on the Elliptic Fourier Descriptor structural similarity index (EFD-SSIM), which can evaluate the clustering stability only when the clustering result is available. Its mechanism is that cluster is mapped into 2D graphics, and the degree of intra-class cohesion is measured based on the structural similarity (SSIM) on the graphics. As shown by the experimental results, EFD-SSIM has a good evaluation effect and it is consistent with the existing effectiveness evaluation indices of the clustering algorithm.

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Section: Efdmentioning

confidence: 99%