Efficient ant colony optimization for image feature selection

Chen, Bolun; Chen, Ling; Chen, Yixin

doi:10.1016/j.sigpro.2012.10.022

Cited by 127 publications

(74 citation statements)

References 38 publications

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“…From then on, many researchers have applied ACO to different optimization problems like vehicle routing [13], transportation planning problems [14], job shop scheduling [15], continuous optimization problems [26], software structural testing [17]. It was used also in image processing [18] for image segmentation [19] [20], image thresholding [21], image edge detection [22] [23], face recognition [24], structural topology optimization [25], optimization of support vector machines parameters' [26], image watermarking algorithm scaling factors' optimization [27] and image feature selection [28]. All these researches have proved the ACO efficiency as a new tool to obtain satisfying optimization results.…”

Section: Related Workmentioning

confidence: 99%

An ant colony based model to optimize parameters in industrial vision

Benchikhi

Sadgal²,

Elfazziki³

et al. 2017

ELCVIA

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Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.

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Section: Related Workmentioning

confidence: 99%

An ant colony based model to optimize parameters in industrial vision

Benchikhi

Sadgal²,

Elfazziki³

et al. 2017

ELCVIA

View full text Add to dashboard Cite

show abstract

“…Ants traverse on the graph to look for a path containing part of the nodes which indicate the feature [20].…”

Section: Aco Based Feature Selectionmentioning

confidence: 99%

“…Furthermore, in [20] an ACO-based feature selection algorithm is presented to reduce the memory requirement and computation time. This algorithm uses the classifier performance and feature set size to guide the search, and optimizes the feature set in terms of its size and classifier performance.…”

Section: Pso Based Feature Selectionmentioning

confidence: 99%

Hybrid ACO-PSO Based Approaches for Feature Selection

Menghour¹,

Souici-Meslati²

2016

IJIES

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Abstract:Feature selection attempts to find the most discriminative information aiming to design an accurate learning system. Feature selection has been the focus of interest for a long time and many works had been done. Recently, the tendency of research in this domain is oriented to the bio-inspired methods. In this paper, we propose hybrid bio-inspired approaches applied to the feature selection problem. The approaches are based on two swarm intelligence methods: ant colony optimization (ACO) and particle swarm optimization (PSO). The performances of these approaches are compared with simple bio-inspired feature selection methods based on ant colony optimization, particle swarm optimization and genetic algorithm. Our experimental results show the efficiency of the proposed approaches in the reduction of selected features number and improvement of classification performance.

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“…Nakamura et al [2] proposed a wrapper method using a BAT algorithm with OPF classifier. Among feature selection approaches to Ant Colony Optimization, we can highlight the ACO for image feature selection proposed by Chen et al [28].…”

Section: Related Concepts and Workmentioning

confidence: 99%

Data feature selection based on Artificial Bee Colony algorithm

2013

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Classification of data in large repositories requires efficient techniques for analysis since a large amount of features is created for better representation of such images. Optimization methods can be used in the process of feature selection to determine the most relevant subset of features from the data set while maintaining adequate accuracy rate represented by the original set of features. Several bioinspired algorithms, that is, based on the behavior of living beings of nature, have been proposed in the literature with the objective of solving optimization problems. This paper aims at investigating, implementing, and analyzing a feature selection method using the Artificial Bee Colony approach to classification of different data sets. Various UCI data sets have been used to demonstrate the effectiveness of the proposed method against other relevant approaches available in the literature.

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Efficient ant colony optimization for image feature selection

Cited by 127 publications

References 38 publications

An ant colony based model to optimize parameters in industrial vision

An ant colony based model to optimize parameters in industrial vision

Hybrid ACO-PSO Based Approaches for Feature Selection

Data feature selection based on Artificial Bee Colony algorithm

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