Integrating agent-based models and clustering methods for improving image segmentation
Image segmentation through clustering is a widely used technique in computer vision that partitions an image into multiple segments by grouping pixels based on feature similarity. Although effective for certain applications, this approach often struggles with the complexity of real-world images, whe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024167291 |
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| author | Erik Cuevas Sonia Jazmín García-De-Lira Cesar Rodolfo Ascencio-Piña Marco Pérez-Cisneros Sabrina Vega |
| author_facet | Erik Cuevas Sonia Jazmín García-De-Lira Cesar Rodolfo Ascencio-Piña Marco Pérez-Cisneros Sabrina Vega |
| author_sort | Erik Cuevas |
| collection | DOAJ |
| description | Image segmentation through clustering is a widely used technique in computer vision that partitions an image into multiple segments by grouping pixels based on feature similarity. Although effective for certain applications, this approach often struggles with the complexity of real-world images, where noise and random variations can significantly affect feature homogeneity, leading to incorrect pixel classifications. To address these limitations, this paper introduces a novel hybrid image segmentation method that combines an agent-based model with a clustering technique to enhance segmentation accuracy and robustness. The method starts with an agent-based model as a preprocessing step aimed at homogenizing pixel intensities within each region. In this model, pixels adjust their intensities based on a consensus reached within their neighborhood, promoting a more uniform feature distribution. Subsequently, the Firefly metaheuristic clustering method is applied to segment the preprocessed image into distinct regions. Metaheuristic techniques, distinguished from classical clustering methods, possess the capability to adaptively navigate through a broad solution space to discover optimal clustering configurations. This adaptability makes them suitable for complex image datasets. The efficacy of the proposed hybrid segmentation method has been tested on various images, employing key quality indices for evaluation. Experimental outcomes demonstrate that this approach yields superior segmented images, showcasing enhanced quality and robustness compared to other segmentation methods. |
| format | Article |
| id | doaj-art-19378539ed624712804826b328990b5a |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-19378539ed624712804826b328990b5a2025-01-17T04:49:44ZengElsevierHeliyon2405-84402025-01-01111e40698Integrating agent-based models and clustering methods for improving image segmentationErik Cuevas0Sonia Jazmín García-De-Lira1Cesar Rodolfo Ascencio-Piña2Marco Pérez-Cisneros3Sabrina Vega4Corresponding author.; Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, MexicoUniversidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, MexicoUniversidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, MexicoUniversidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, MexicoUniversidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, MexicoImage segmentation through clustering is a widely used technique in computer vision that partitions an image into multiple segments by grouping pixels based on feature similarity. Although effective for certain applications, this approach often struggles with the complexity of real-world images, where noise and random variations can significantly affect feature homogeneity, leading to incorrect pixel classifications. To address these limitations, this paper introduces a novel hybrid image segmentation method that combines an agent-based model with a clustering technique to enhance segmentation accuracy and robustness. The method starts with an agent-based model as a preprocessing step aimed at homogenizing pixel intensities within each region. In this model, pixels adjust their intensities based on a consensus reached within their neighborhood, promoting a more uniform feature distribution. Subsequently, the Firefly metaheuristic clustering method is applied to segment the preprocessed image into distinct regions. Metaheuristic techniques, distinguished from classical clustering methods, possess the capability to adaptively navigate through a broad solution space to discover optimal clustering configurations. This adaptability makes them suitable for complex image datasets. The efficacy of the proposed hybrid segmentation method has been tested on various images, employing key quality indices for evaluation. Experimental outcomes demonstrate that this approach yields superior segmented images, showcasing enhanced quality and robustness compared to other segmentation methods.http://www.sciencedirect.com/science/article/pii/S2405844024167291Metaheuristic algorithmHybridizationABMAgentsFireflyImage processing |
| spellingShingle | Erik Cuevas Sonia Jazmín García-De-Lira Cesar Rodolfo Ascencio-Piña Marco Pérez-Cisneros Sabrina Vega Integrating agent-based models and clustering methods for improving image segmentation Heliyon Metaheuristic algorithm Hybridization ABM Agents Firefly Image processing |
| title | Integrating agent-based models and clustering methods for improving image segmentation |
| title_full | Integrating agent-based models and clustering methods for improving image segmentation |
| title_fullStr | Integrating agent-based models and clustering methods for improving image segmentation |
| title_full_unstemmed | Integrating agent-based models and clustering methods for improving image segmentation |
| title_short | Integrating agent-based models and clustering methods for improving image segmentation |
| title_sort | integrating agent based models and clustering methods for improving image segmentation |
| topic | Metaheuristic algorithm Hybridization ABM Agents Firefly Image processing |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024167291 |
| work_keys_str_mv | AT erikcuevas integratingagentbasedmodelsandclusteringmethodsforimprovingimagesegmentation AT soniajazmingarciadelira integratingagentbasedmodelsandclusteringmethodsforimprovingimagesegmentation AT cesarrodolfoascenciopina integratingagentbasedmodelsandclusteringmethodsforimprovingimagesegmentation AT marcoperezcisneros integratingagentbasedmodelsandclusteringmethodsforimprovingimagesegmentation AT sabrinavega integratingagentbasedmodelsandclusteringmethodsforimprovingimagesegmentation |