用萤火虫算法分割图像

Akash Sharma, Smriti Sehgal
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引用次数: 19

摘要

图像分割是图像处理领域的一个重要步骤,它将图像分割成若干部分,这些部分为用户提供特定类型的信息。图像分割是图像处理的难点,其目的是提取图像中的信息。聚类用于分割图像。聚类算法是数据挖掘算法的一部分,它将数据分组到不同数量的给定聚类中。一个聚类中的所有数据点都有相似的属性,即每个聚类的点之间的差异最小,与其他聚类数据点的差异最大。该算法使用k-mean算法和萤火虫算法将图像像素聚类到k个聚类中进行分割。由于k均值聚类算法陷入局部最优,采用萤火虫算法进行优化。基于群体智能的算法构成了萤火虫算法的基础,萤火虫算法具有多种应用并用于解决优化问题。萤火虫算法已应用于许多研究和优化领域。萤火虫算法及其杂交版本已经成功地解决了各种问题。为了将萤火虫算法应用于更广泛的问题领域,必须对萤火虫算法进行改进或与其他算法相结合。目前,算法的元启发式性质在求解np困难问题中发挥着重要作用,目前的优化算法包含了这种性质,并且非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image segmentation using firefly algorithm
Image segmentation is an important step in the domain of image processing in which we segment the image into several parts which carry certain type of information for the user. Image segmentation is very difficult step in the processing of the image which aims at extracting the information from image. Clustering is used to segment the image. Clustering algorithms are part of data mining algorithm that groups the data into various number of given clusters. All the data points in one cluster have similar properties based on which they are clustered i.e. each cluster has minimum difference between its points and maximum difference from other cluster data points. The proposed algorithm uses k-mean algorithm and firefly to cluster image pixels into k cluster for segmentation. Since k-mean clustering algorithm is gets trapped in local optima it is optimized using firefly algorithm. Swarm intelligence based algorithms forms the basis of the firefly algorithm which has several application and used to solve optimization problems. Firefly algorithm has been applied in many research and optimization areas. Firefly algorithm and its hybridized version have been used to solve various problems successfully. To apply firefly algorithm to wide areas of problem the firefly algorithm must be modified or integrated with other algorithms. Presently metaheuristic nature of algorithm plays an important role and current optimization algorithm include this nature and are very efficient in solving NP-hard problems.
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