A New Evaluation Method for Mesh Segmentation Based on the Levenshtein Distance

Zakani, Fatima Rafii; Arhid, Khadija; Bouksim, Mohcine; Mohamed, Azah; Gadi, Taoufiq

doi:10.15866/irecos.v11i12.10922

Cited by 2 publications

(2 citation statements)

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“…Recently, our research team proposed six quantitative evaluation metrics which are: WDC [29], WKD [30], WSSD [31], Dj3D [32], WOI [33], NWLD [34]. The proposed evaluation measures are based respectively on: The Dice's coefficient, the Kulczynski similarity index, Sokal -Sneath distance, The Jaro distance, the Ochiai index and the final one is based on the Levenshtein Distance.…”

Section: Fig 3 the Segmentation Results Of Different 3d Meshes Usinmentioning

confidence: 99%

Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering

Zakani¹,

Bouksim²,

Arhid³

et al. 2018

Computer Optics

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3D mesh segmentation has become an essential step in many applications in 3D shape analysis. In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation. Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the benchmark proposed by Chen et al. to extract the candidate boundaries of a given 3D-model based on a set of geometric criteria. Then, we use this resulted knowledge to construct a new connectivity of the mesh and use the spectral clustering method to segment the 3D mesh into significant parts. Our approach was evaluated using different evaluation metrics. The experiments confirm that the proposed method yields significantly good results and outperforms some of the competitive segmentation methods in the literature.

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Section: Fig 3 the Segmentation Results Of Different 3d Meshes Usinmentioning

confidence: 99%

Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering

Zakani¹,

Bouksim²,

Arhid³

et al. 2018

Computer Optics

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show abstract

“…It is clear that the most critical challenge in this type of approach is how to segment a 3D object into meaningful parts. To determine which segmentation method we will use, we have evaluated different segmentation algorithms by using some of the recent evaluation methods proposed in the literature, which give an efficient quantitative evaluation by comparing the automatic segmentation with a set of ground truth segmentations instead of one to one comparison, these evaluation methods are: NWLD [23], WKD [24], WDC [25], WOI [26], WSSD [27], Dj3D [28] and the AEI [29]. Table 1 present for each segmentation method its average of the dissimilarity scores obtained for the entire database by using the cited evaluation methods.…”

Section: Our Proposed Approachmentioning

confidence: 99%

A novel approach for partial shape matching and similarity based on data envelopment analysis

et al. 2019

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Due to the growing number of 3D objects in digital libraries, the task of searching and browsing models in an extensive 3D database has been the focus of considerable research in the area. In the last decade, several approaches to retrieve 3D models based on shape similarity have been proposed. The majority of the existing methods addresses the problem of similarity between objects as a global matching problem. Consequently, most of these techniques do not support a part of the object as a query, in addition to their poor performance for classes with globally non-similar shape models and also for articulated objects. The partial matching technique seems to be a suitable solution to these problems. In this paper, we address the problem of shape matching and retrieval. We propose a new approach based on partial matching in which each 3D object is segmented into its constituent parts, and shape descriptors are computed from these elements to compare similarities. Several experiments investigated that our technique enables fast computing for content-based 3D shape retrieval and significantly improves the results of our method based on Data Envelopment Analysis descriptor for global matching.

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A New Evaluation Method for Mesh Segmentation Based on the Levenshtein Distance

Cited by 2 publications

References 0 publications

Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering

Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering

A novel approach for partial shape matching and similarity based on data envelopment analysis

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