A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

Mittal, Himanshu; Pandey, Avinash Chandra; Saraswat, Mukesh; Kumar, Sumit; Pal, Raju; Modwel, Garv

doi:10.1007/s11042-021-10594-9

Cited by 98 publications

(40 citation statements)

References 68 publications

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“…From this low-level classification, we can reconstruct a segmentation mask, which we call the detection mask. A considerable number of models have been proposed in the literature to deal with the problem of pixel classification [21,22]. However, as the study and development of models are not the purpose of the present work, we experimented with only three models that have been shown in the literature to provide good results in agricultural contexts:…”

Section: Three Segmentation Models For Experimentationmentioning

confidence: 99%

Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing

et al. 2022

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A considerable number of metrics can be used to evaluate the performance of machine learning algorithms. While much work is dedicated to the study and improvement of data quality and models’ performance, much less research is focused on the study of these evaluation metrics, their intrinsic relationship, the interplay of the influence among the metrics, the models, the data, and the environments and conditions in which they are to be applied. While some works have been conducted on general machine learning tasks such as classification, fewer efforts have been dedicated to more complex problems such as object detection and image segmentation, in which the evaluation of performance can vary drastically depending on the objectives and domains of application. Working in an agricultural context, specifically on the problem of the automatic detection of plants in proximal sensing images, we studied twelve evaluation metrics that we used to evaluate three image segmentation models recently presented in the literature. After a unified presentation of these metrics, we carried out an exploratory analysis of their relationships using a correlation analysis, a clustering of variables, and two factorial analyses (namely principal component analysis and multiple factorial analysis). We distinguished three groups of highly linked metrics and, through visual inspection of the representative images of each group, identified the aspects of segmentation that each group evaluates. The aim of this exploratory analysis was to provide some clues to practitioners for understanding and choosing the metrics that are most relevant to their agricultural task.

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Section: Three Segmentation Models For Experimentationmentioning

confidence: 99%

Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing

et al. 2022

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“…• BSDS500 dataset [26], available at [4], has been used for testing L 0 in [23]. It is sufficiently general and provides a large variety of images often employed for testing many other methods with different image analysis tasks such as image segmentation [29,39,40,42]), color quantization [12,41,10,43], etc.…”

Section: Datasetsmentioning

confidence: 99%

Lagrange-Chebyshev Interpolation for image resizing

Ramella¹,

Themistoclakis²

2021

Preprint

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Image resizing is a basic tool in image processing and in literature we have many methods, based on different approaches, which are often specialized in only upscaling or downscaling. In this paper, independently of the (reduced or enhanced) size we aim to get, we approach the problem at a continuous scale where the underlying continuous image is globally approximated by the tensor product Lagrange polynomial interpolating at a suitable grid of first kind Chebyshev zeros. This is a well-known approximation tool that is widely used in many applicative fields, due to the optimal behavior of the related Lebesgue constants. Here we show how Lagrange-Chebyshev interpolation can be fruitfully applied also for resizing an arbitrary digital image in both downscaling and upscaling. The performance of the proposed method has been tested in terms of the standard SSIM and PSNR metrics. The results indicate that, in upscaling, it is almost comparable with the classical Bicubic resizing method with slightly better metrics, but in downscaling a much higher performance has been observed in comparison with Bicubic and other recent methods too. Moreover, in downscaling cases with an odd scale factor, we give an estimate of the mean squared error produced by our method and prove it is theoretically null (hence PSNR equals to infinite and SSIM equals to one) in absence of noise or initial artifacts on the input image.

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“…In particular, we consider BSDS500 dataset [27], available at [71] which includes 500 color images having the same size (481×321 or 321×481). This set, also used in [23,34], is sufficiently general and provides a large variety of images often employed also in other different image analysis tasks, such as in image segmentation [32,[44][45][46] and in color quantization [6,8,48,49].…”

Section: Datasetsmentioning

confidence: 99%

Image scaling by de la Vallée-Poussin filtered interpolation

Ramella¹,

Themistoclakis²

2021

Preprint

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We present a new image scaling method both for downscaling and upscaling, running with any scale factor or desired size. It is based on the sampling of an approximating bivariate polynomial, which globally interpolates the data and is defined by a filter of de la Vallée Poussin type whose action ray is suitable regulated to improve the approximation. The method has been tested on a significant number of different image datasets. The results are evaluated in qualitative and quantitative terms and compared with other available competitive methods. The perceived quality of the resulting scaled images is such that important details are preserved, and the appearance of artifacts is low. Very high-quality measure values in downscaling and the competitive ones in upscaling evidence the effectiveness of the method. Good visual quality, limited computational effort, and moderate memory demanding make the method suitable for real-world applications.

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A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

Cited by 98 publications

References 68 publications

Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing

Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing

Lagrange-Chebyshev Interpolation for image resizing

Image scaling by de la Vallée-Poussin filtered interpolation

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