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In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results. [ABSTRACT FROM PUBLISHER]

Image segmentation has been widely used in different applications of the image processing. The main objective of image segmentation is to subdivide the input images to their main components. Generally, the main purpose of the segmentation is to simplify or change an image representation into something that is more meaningful and easier to analyze. In this paper, World Cup Optimization Algorithm (WCO) is proposed to classify the main components of an image (pixels) into different groups. In the experiment, the proposed method performance is measured by comparing with Otsu as a classic method and GA based and APSO based image segmentation algorithms as the heuristic based algorithms for segmentation. When compared with the other segmentation methods, the proposed WCO based method achieved good performance. The final efficiency of the proposed system is compared with the described methods. Experimental results show that the proposed method has overcome the others in the performance. [ABSTRACT FROM AUTHOR]

Image segmentation process is one of the most interesting and challenging problems in digital image processing tasks. The segmentation process involves finding similar regions within an image. Many segmentation problems are achieved by the incorporation of clustering techniques. One of the most common technique for clustering process is the Fuzzy C-means (FCM) algorithm. However, even when FCM is one of the most popular techniques applied in image segmentation, it presents some issues such as large computational time complexity, noise sensitivity, and initial cluster centers dependency. In order to solve these problems, this paper presents a Histogram Based Fuzzy Clustering (HBFC) technique using an improved version of Firefly Algorithm (FA). In the proposed approach, the FA involves three search strategies: rough set-based population, random attraction and local search mechanism. Also, the clustering process is conducted based on gray level histograms instead of single pixels of an image. Under such circumstances, the occurrence of misclassification of pixels is reduced. A rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperform the well-known NIOA based clustering methods in terms of precision, robustness and quality of the segmented outputs. • An improved Firefly algorithm has been proposed for Image Clustering. • Random Attraction model and rough set have been utilized. • The proposed approach is a histogram based fast fuzzy clustering model. • The proposed method is compared against state-of-art segmentation methods. • Numerical results indicate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]