Modified JSEG algorithm for reducing over-segmentation problems in underwater coral reef images

Aloun, Mohammad Sameer; Hitam, Muhammad Suzuri; Yussof, Wan NuralJawahir Hj Wan; Hamid, Abdul Aziz K Abdul; Bachok, Zainuddin

doi:10.11591/ijece.v9i6.pp5244-5252

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…The experimental results of color image segmentation using MATLAB 2019b with a CPU Intel Core i7-4710HQ, the VOC2012 dataset [37] and the BSDS500 dataset [38] collected to measure the performance of PMLCD compared with other unsupervised machine learning methods including K-means [2,3], mean shift [4,5], and JSEG [6,7] and classical methods including the grayscale PMVIF [21][22][23], grayscale watershed [26,27], and SLIC [34,35] are given in this section. Benchmarks used in this paper include the Rand Index (RI) [39], Global Consistency Error (GCE) [39], Normalized Variation of information (NVI) [40], Boundary Displacement Error (BDE) [39], Dice coefficient [39], computation time, and noise tolerance.…”

Section: Resultsmentioning

confidence: 99%

“…where C is a normalization factor making max | n(i, j)| = 1, and (x (i,j) ,ȳ (i,j) ) is a centroid, computed using Equation ( 5), of LCD(x − i, y − j) computed using Equation (7). Figure 3b demonstrates the n of the image in Figure 3a.…”

Section: The Normal Compressive Vector Fieldmentioning

confidence: 99%

“…Instead, the result of each iteration is recursively input to the program to adjust its parameters. This type of approach, such as K-means [2,3], mean shift [4,5], and JSEG [6,7], etc., is often more effective and more tolerant of unusual or unpredictable situations. However, the unsupervised methods are usually time-consuming due to its iterative processes embedded in the methods.…”

Section: Introductionmentioning

confidence: 99%

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Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images

Phornphatcharaphong

Eua-Anant

2020

J. Imaging

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This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vector field is created from the collection of the center-to-centroid vectors of local color distance images. The edge vector field is later derived from the normal compressive vector field so as to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012), the Berkeley Segmentation Data Set, and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison to those of the traditional particle motion in a vector image field (PMVIF), Watershed, simple linear iterative clustering (SLIC), K-means, mean shift, and J-value segmentation (JSEG). The proposed method yields better Rand index (RI), global consistency error (GCE), normalized variation of information (NVI), boundary displacement error (BDE), Dice coefficients, faster computation time, and noise resistance.

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Section: Resultsmentioning

confidence: 99%

Section: The Normal Compressive Vector Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images

Phornphatcharaphong

Eua-Anant

2020

J. Imaging

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“…JSEG is a powerful unsupervised segmentation algorithm for color images that proved its effectiveness and robustness in a variety of applications [37,38]. JSEG has recently witnessed various improvements to improve its performances, such as in the problem of oversegmentation [16,39]. In our study, the JSEG proposed in [18] has been employed to segment the image into a set of semantic regions, as illustrated in Figure 2.…”

Section: Image Segmentation Using Jseg Algorithmmentioning

confidence: 99%

Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation

et al. 2021

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Automatic image annotation is an active field of research in which a set of annotations are automatically assigned to images based on their content. In literature, some works opted for handcrafted features and manual approaches of linking concepts to images, whereas some others involved convolutional neural networks (CNNs) as black boxes to solve the problem without external interference. In this work, we introduce a hybrid approach that combines the advantages of both CNN and the conventional concept-to-image assignment approaches. J-image segmentation (JSEG) is firstly used to segment the image into a set of homogeneous regions, then a CNN is employed to produce a rich feature descriptor per area, and then, vector of locally aggregated descriptors (VLAD) is applied to the extracted features to generate compact and unified descriptors. Thereafter, the not too deep clustering (N2D clustering) algorithm is performed to define local manifolds constituting the feature space, and finally, the semantic relatedness is calculated for both image–concept and concept–concept using KNN regression to better grasp the meaning of concepts and how they relate. Through a comprehensive experimental evaluation, our method has indicated a superiority over a wide range of recent related works by yielding F1 scores of 58.89% and 80.24% with the datasets Corel 5k and MSRC v2, respectively. Additionally, it demonstrated a relatively high capacity of learning more concepts with higher accuracy, which results in N+ of 212 and 22 with the datasets Corel 5k and MSRC v2, respectively.

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Semantic Image Analysis for Automatic Image Annotation

Minaoui

Oujaoura

2021

Advances in Intelligent Systems and Computing

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Modified JSEG algorithm for reducing over-segmentation problems in underwater coral reef images

Cited by 3 publications

References 18 publications

Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images

Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images

Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation

Semantic Image Analysis for Automatic Image Annotation

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