Swarm Intelligence and Image Segmentation (original) (raw)
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A Review on Image Segmentation using Clustering and Swarm Optimization Techniques
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
An unsupervised multi-swarm clustering technique for image segmentation
Swarm and Evolutionary Computation, 2013
Methods based on Particle Swarm Optimization represent efficient tools to solve a wide class of problems. In particular, they have been successfully applied to data clustering and image processing. In this paper a multi-swarm clustering technique to perform an image segmentation is proposed. The search of the gray levels segmenting the image is carried out by a two-stage procedure. The former is performed by a traditional swarm population, moving in the search space according to a minimum distance criterion. The latter exploits a structure composed by identical swarms that refine the solution of the previous step. The combination of the two swarm approaches allows to tackle the drawbacks of the classical paradigm without making use of a complex implementation. The method is unsupervised, since it identifies the actual number of gray levels to segment the image automatically. Such characteristic is fundamental in the application of image segmentation to real cases, where generally the optimal number of centers is not known a priori and the algorithms are required to face possible environment variations. The conducted experiments show that the proposed technique is able to yield adequate segmentations with a limited computational time, proving to be an interesting tool to face cases in which urgent time constraints have to be satisfied.
Emerging Applications of Bio-Inspired Algorithms in Image Segmentation
Electronics
Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting para...
Image segmentation based on a new self-adaptive ant clustering algorithm
2010
Image segmentation can be considered as the process of clustering image pixels of different image features. Clustering algorithm based on ant behaviors is a parallel, self-organized algorithm with sound discreteness, positive feedback and robustness. The basic ant colony algorithm is redundant in futile program loop, random in search mechanism and of which, the result is sensitive to the initial parameters. This paper improves these defects and suggests the idea of hierarchical clustering method. Then it applies the improved algorithm to image segmentation with the feature vector comprising of grayscale, gradient and neighborhood. Analytically, the improved algorithm has such merits as fast convergence, clustering effect of high quality, high clustering efficiency and robustness.
International Journal for Research in Applied Science & Engineering Technology, 2021
Image edge detection mainly involves in identifying points in an image at which the image brightness changes sharply or more formally has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. Edges give important structural information about the images which would play a vital role in various fields of image processing and computer vision. In order to optimize the process of image edge detection and produce better edges for images, we have implemented edge detection using two swarm intelligence techniques. In this chapter we will study in detail how ant colony optimisation and particle swarm optimization techniques were utilised to identify edges of an image. I. INTRODUCTION The concept of edge detection mainly involves in identifying local variation in brightness levels in images. Image edge detection is mainly employed for image processing and computer vision. Computer vision helps in extracting useful information from the detected objects such as images, videos. In last two decades numerous techniques and variations have been developed. Despite them being carried out, main skeleton of these techniques remains similar. With varying application domains, most of these techniques have been modified to fit into various applications. The changes undergo mostly in perspective of encoding scheme, parameter tuning and search strategy. The edge detection task is to find the boundaries of image regions based on properties such as intensity and texture [3]. It is a critical low-level procedure of image processing because edges carry a lot of information. The edge detection process generally includes five steps: 1) Filtering: Filtering out noise from the image and improving the performance of the edge detector. 2) Enhancement: Emphasising pixels which have important change in local intensity. 3) Detection: Identifying the edges and thresholding. 4) Link: Linking the broken edges (such as hysteresis thresholding techniques). 5) Localisation: Locating the edge accurately and estimating the edge orientation (edge and orientation map). In the modern age computer vision concepts are useful for detecting checks through images and in automated driving cars. Edge detection helps in identifying the important structural object of the image, which thereby is used for identifying important information from image such as number, size, and relative location of objects in an image. If we compare black and white grayscale images the variation in colour for our eyes is very sudden but at pixel level, it is not very rapid. The edge detected is a constant change of pixel values when we travel from one bright level to another bright level in the space of pixels of an image. Usually, the technique for identifying variations in the pixel values to identify edge variation is calculated for each pixel value in image space which is not an optimistic solution for identifying an image. So, to optimize the solution for this scenario in this paper we will further discuss about implementing swarm intelligence for detecting edges. Swarm intelligence mainly involves the study of collective and individual behaviour of biological systems which help in improving efficiency for solving complex problems such as identifying shortest path for food source or identifying their enemies in nature. Even though all the insects are unsophisticated and unorganised they make wonders by adapting to their problems in environment and solving their daily life scenarios.
Clustering Amelioration and Optimization with Swarm Intelligence for Color Image Segmentation
International Journal of Database Theory and Application, 2015
Cluster examination is data mining task for the assignment of collection a set of items in such a path, to the point that questions in the same gathering (called a cluster) are more like one another than to those in different gatherings (clusters). K-means grouping is a technique for group investigation which intends to parcel n perceptions into k groups in which every perception fits in with the cluster with the closest mean. This paper, decided the aftereffect of standard parameter estimations of shading picture division with k-means and the modified k-means with ABC and ACO algorithms.The paper demonstrates that division of color picture with modified k-mean consolidated with swarm Intelligence calculations for color image segmentation gives preferable results over simple k-means and Modified k-means with Ant colony optimization gives better results than modified k-means with Artificial bee colony .
AASC: Advanced Ant based Swarm Computing for detection of edges in Imagery
The social insect allegory for working out problems has become a promising area in the recent years emphasizing on stochastic construction practice building the key probabilistically. The approach focuses on direct or indirect communications among uncomplicated agents. Swarm Intelligence is the collective behavior of decentralized, self-organized system whereby the joint behavior of agent interacting locally with the environment causes coherent global pattern to emerge. Ant Colony Optimization (ACO) is an algorithm inspired by the foraging behavior of ants wherein ants leaves a volatile substance call pheromone on the soil surface for the purpose of foraging and collective interaction via indirect communication. Edge detection mainly is the set of mathematically methods aiming to identify points in an image at which the image brightness changes sharply or formally generating some formation of discontinuities. This paper explores the Swarm computing technique called Ant Colony Optimization (ACO) and further proposes a new technique called as Advanced Ant based Swarm Computing (AASC) for edge detection of imagery.
A Competitive Swarm Algorithm for Image Segmentation Guided by Opposite Fuzzy Entropy
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020
This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.