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An iterative method to improve the results of ant-tree algorithm applied to colour quantisation

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2018年第2期12卷 87-114页

作者： Perez-Delgado, Maria-Luisa Univ Salamanca Escuela Politecn Super Zamora Dept Comp Sci Av Requejo 33 Zamora 49022 Spain

Colour quantisation methods attempt to represent a colour image by a palette with fewer colours than the original one. This paper presents a method of this type, based on a previous algorithm called ant-tree for colour quantisation (ATCQ), which applies artificial ants for colour reduction. An important advantage of the new algorithm is that it does not require sorting the input data. Moreover, it applies an iterative process to increase the quality of the quantised image as iterations proceed. The proposed algorithm gives better results than the original one, and it is competitive with other well-known colour quantisation methods, such as Median-cut, Octree, Neuquant, variance-based, binary splitting or Wu's methods.

关键词： colour quantisation clustering artificial ants ant-tree algorithm colour image processing colour reduction bio-inspired computation

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Color quantization with Particle swarm optimization and artificial ants

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SOFT COMPUTING 2020年第6期24卷 4545-4573页

作者： Perez-Delgado, Maria-Luisa Univ Salamanca Escuela Politecn Super Zamora Av Requejo 33 Zamora 49022 Spain

This article describes a color quantization algorithm that combines two swarm-based methods: Particle swarm optimization and artificial ants. The proposed method is based on a previous method that solves the quantization problem by combining the Particle swarm optimization algorithm with the K-means algorithm. K-means is a popular clustering method that has been applied to solve a variety of problems, including the color quantization problem. Nevertheless, it is a time-consuming method, which makes combining the Particle swarm optimization algorithm and K-means less suitable than other color quantization techniques. The proposed method, however, discards the K-means algorithm and applies the ant-tree for color quantization algorithm in order to reduce execution time. This article shows that the new method outperforms the original one, since it requires less time to obtain higher quality images. In addition, the images produced are also of better quality than those produced by other well-known color quantization methods, such as Neuquant, Octree, Median-cut, Variance-based, Binary splitting and Wu's methods.

关键词： Color quantization Artificial ants ant-tree algorithm Particle swarm optimization algorithm Clustering

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A two-stage method to improve the quality of quantized images

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2020年第3期17卷 581-605页

作者： Perez-Delgado, Maria-Luisa Roman Gallego, Jesus-Angel Univ Salamanca Escuela Politecn Super Zamora Ave Requejo 33 Zamora 49022 Spain

This article proposes a color quantization strategy that combines two color quantization methods: Binary Splitting and ant tree for Color Quantization. This solution combines a splitting method, which is faster, and a clustering-based method, which generates better quantized images. Given that time is a fundamental factor when considering a method for real-time applications, the proposed strategy attempts to exploit both of these methods for obtaining good quantized images with a low computational cost. The result of this approach not only generates better images than when Binary Splitting and ant tree for Color Quantization are applied separately, but also helps to improve other methods frequently used for color quantization such as Wu's method, Octree, Variance-based method and Neuquant.

关键词： Color quantization Binary Splitting algorithm Artificial ants ant-tree algorithm Clustering

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The color quantization problem solved by swarm-based operations

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APPLIED INTELLIGENCE 2019年第7期49卷 2482-2514页

作者： Perez-Delgado, Maria-Luisa Univ Salamanca Escuela Politecn Super Zamora Ave Requejo 33 Zamora 49022 Spain

The objective of the color quantization problem is to reduce the number of different colors of an image, in order to obtain a new image as similar as possible to the original. This is a complex problem and several solution techniques have been proposed to solve it. Among the most novel solution methods are those that apply swarm-based algorithms. These algorithms define an interesting solution approach, since they have been successfully applied to solve many different problems. This paper presents a color quantization method that combines the Artificial Bee Colony algorithm with the ant-tree for Color Quantization algorithm, creating an improved version of a previous method that combines artificial bees with the K-means algorithm. Computational results show that the new method significantly reduces computing time compared to the initial method, and generates good quality images. Moreover, this new method generates better images than other well-known color quantization methods such as Wu's method, Neuquant, Octree or the Variance-based method.

关键词： Color quantization Artificial ants ant-tree algorithm Artificial bee colony algorithm Clustering

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Artificial ants and fireflies can perform colour quantisation

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APPLIED SOFT COMPUTING 2018年 73卷 153-177页

作者： Perez-Delgado, Maria-Luisa Univ Salamanca Escuela Politecn Super Zamora Av Requejo 33 Zamora 49022 Spain

Several methods based on the behaviour of biological systems have been proposed during the last years to solve a wide range of problems. ant algorithms and Firefly algorithms are two methods of this type. The ant-tree for Colour Quantisation algorithm was recently proposed to quantise colour images. It is a clustering-based method which creates a tree structure where the pixels of the image are grouped in subtrees according to their similarity. A threshold value determines if the similarity is sufficient to associate a pixel to a subtree. The computation of that threshold includes a parameter in (0, 1], whose value must be set taking into account the number of colours of the quantised image. Since this parameter influences the quality of the final image, it is recommended to perform several tests with different values of that parameter and then select the best result. In order to reduce the influence of said parameter and improve the resulting image, this paper proposes to combine the ant-tree for Colour Quantisation algorithm with the Firefly algorithm. Computational experiments show that the new method generates better images than some popular colour quantisation methods such as Median-cut, Octree, Neuquant, Wu's method or the Variance-based method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Colour quantisation Artificial ants ant-tree algorithm Firefly algorithm Clustering

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Colour quantization with ant-tree

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APPLIED SOFT COMPUTING 2015年 36卷 656-669页

作者： Perez-Delgado, M. L. Univ Salamanca Escuela Politecn Super Zamora Zamora 49022 Spain

ant-tree is a clustering algorithm inspired by biological ants. This paper defines a variant of such algorithm to perform colour quantization. Some of the features of the basic ant-tree have been adapted to obtain a quicker algorithm and to perform the main steps of colour quantization on a big input set. The centroid of every cluster defines a colour of the palette and once the complete colour palette is established, the algorithm represents each pixel of the original image by the colour associated to its cluster. Computational results show that the error obtained for the quantized images is smaller than the error generated by some other well-known quantization methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Color quantization Clustering ant-tree algorithm

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Image segmentation with a fuzzy clustering algorithm based on ant-tree

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SIGNAL PROCESSING 2008年第10期88卷 2453-2462页

作者： Yang, Xiaochun Zhao, Weidong Chen, Yufei Fang, Xin Tongji Univ Res Ctr CAD Shanghai 200092 Peoples R China

This paper presents a fuzzy clustering approach for image segmentation based on ant-tree algorithm, which is inspired from the ants' self-assembling behavior. Three features including the gray value, gradient and neighborhood of pixels are extracted for clustering. A three-level tree model is proposed to make the clustering structure more adaptive for image segmentation. Center approximation is employed to optimize the fuzzy Clustering process when building the tree structure. Besides, we present a new initialization method by making use of the histogram of the image. Experiments and comparisons show the effectiveness and the efficiency of the proposed approach. (C) 2008 Elsevier B.V. All rights reserved.

关键词： ant-tree algorithm image segmentation three-level tree center approximation

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Graphical Symbol Recognition in Architectural Plans with an Improved ant-tree based Clustering algorithm

Graphical Symbol Recognition in Architectural Plans with an ...

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International Joint Conference on Neural Networks

作者： Yang, Xiaochun Zhao, Weidong Pan, Li Tongji Univ CAD Res Ctr Shanghai 200092 Peoples R China Tongji Univ Cent Res CAD Shanghai 200030 Peoples R China

ISBN: (纸本)9781424418206

In this paper, an improved clustering algorithm based ant-tree is used for recognition of certain kind of architectural symbols with prior knowledge in engineering drawings. Symbols are segmented from an AutoCAD format drawing and a vector of invariants based on pseudo-Zernike moments is calculated to represent the graphical feature of a symbol. A normalization method is used to make these moments invariant of translation, rotation and scaling. Then the improved ant-tree algorithm is applied to cluster the symbols with regard to their features. The class of target symbols can thus be got easily with the guidance of some prior knowledge. For the proposed clustering algorithm, a new initialization method is presented with regard to the distribution of the data, and centroid approximation is also utilized to optimize the clustering process. Experiments show the effectiveness of our recognition approach proposed.

关键词： architectural symbols recognition pseudo-Zernike moments ant-tree algorithm feature representation

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