Clustering method and sine cosine algorithm for image segmentation

Khrissi, Lahbib; Akkad, Nabil El; Satori, Hassan; Satori, Khalid

doi:10.1007/s12065-020-00544-z

Cited by 42 publications

(23 citation statements)

References 41 publications

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“…These clustering algorithms are the most common used clustering algorithms in different fields because of their efficiency compared to other algorithms. 21,[23][24][25][26][27] 2.1.1 | K-means clustering K-means is a clustering algorithm that introduced in 1967 by MacQueen. 28 The procedure to cluster the data is based on defining a certain number of clusters (K).…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

et al. 2021

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Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. Several low performance intelligent solar tracking systems have been designed and implemented. Multilayer perceptron (MLP) is one of the common controllers that used to drive solar tracking systems. However, when the input data are complex for neural network, neural network would not well explain the relationship between these data. Thus, it performed worse than when the input data are simple. Using a premapping of relationship between samples of data as input to neural network along with the original input data could probably a strong guide to help neural network to reach the desired goal and predict the output variables faster and more accurate. It is found that using the output of logistic regression as input to neural network would faster the process of finding the predicted output by neural network. Thus, this study aims to propose new efficient and low complexity single and dual axis solar tracking systems by integrating supervised logistic regression (LR) and supervised MLP or cascade multilayer perceptron (CMLP). LR models are trained by using one of unsupervised clustering techniques (k-means, fuzzy c-means, and hierarchical clustering algorithms). The

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Section: Clustering Algorithmsmentioning

confidence: 99%

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

et al. 2021

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“…Abdel-Basset et al [23] have remarkably utilized SCA to tackle multi-objective problems for real-time task scheduling in multiprocessor systems. Khrissi et al [24] have smartly incorporated SCA with clustering methods to revolutionize the field of image segmentation. Meanwhile, Kumar & Hussain [25] have judiciously merged SCA, where the Cauchy and Gaussian strategies efficiently optimized global exploration and local exploitation ability, respectively.…”

Section: Introductionmentioning

confidence: 99%

Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

chun

2023

Preprint

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The Sine Cosine Algorithm (SCA) is a well-known optimization technique that utilizes sine and cosine functions to identify optimal solutions. Despite its popularity, the SCA has limitations in terms of low diversity, stagnation in local optima, and difficulty in achieving global optimization, particularly in complex large-scale problems. In response, we propose a novel approach named the Improved Weight and Strategy Sine Cosine Algorithm (IWSCA). The IWSCA achieves this by introducing novel self-adaptive weight and social strategies that enable the algorithm to efficiently search for optimal solutions in complex large-scale problems. The performance of the IWSCA is evaluated with 23 benchmark test functions and the IEEE CEC 2019 benchmark suite, compare it to a state-of-the-art heuristic algorithm and two improved versions of the SCA. Our experimental results demonstrate that the IWSCA outperforms existing methods in terms of convergence precision and robustness.

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“…Many image segmentation algorithms have been proposed in the literature, from the traditional techniques, such as thresholding [ 30 , 31 , 32 , 33 ], edge-based segmentation [ 34 , 35 ], histogram-based bundling, region-based segmentation [ 36 , 37 , 38 , 39 ], clustering-based segmentation [ 40 , 41 , 42 , 43 , 44 ], watershed methods [ 45 , 46 , 47 , 48 , 49 ], to more advanced algorithms such as active contours [ 50 , 51 , 52 , 53 ], graph cuts [ 54 , 55 , 56 , 57 ], conditional and Markov random fields [ 58 , 59 , 60 , 61 ], and sparsity-based methods [ 62 , 63 , 64 ].…”

Section: Introductionmentioning

confidence: 99%

Graphical Image Region Extraction with K-Means Clustering and Watershed

Jardim

António²,

Mora

2022

J. Imaging

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With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.

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Clustering method and sine cosine algorithm for image segmentation

Cited by 42 publications

References 41 publications

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

Graphical Image Region Extraction with K-Means Clustering and Watershed

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