Cluster ensemble has been shown to be very effective in unsupervised classification learning by generating a large pool of different clustering solutions and then combining them into a final decision. However, the task of it becomes more difficult due to the inherent complexities among base cluster results, such as uncertainty, vagueness and overlapping. Granular computing is one of the fastest growing information-processing paradigms in the domain of computational intelligence and human-centric systems. As the core part of granular computing, the rough set theory dealing with inexact, uncertain, or vague information, has been widely applied in machine learning and knowledge discovery related areas in recent years. From these perspectives, in this paper, a hierarchical cluster ensemble model based on knowledge granulation is proposed with the attempt to provide a new way to deal with the cluster ensemble problem together with ensemble learning application of the knowledge granulation. A novel rough distance is introduced to measure the dissimilarity between base partitions and the notion of knowledge granulation is improved to measure the agglomeration degree of a given granule. Furthermore, a novel objective function for cluster ensembles is defined and the corresponding inferences are made. A hierarchical cluster ensemble algorithm based on knowledge granulation is designed. Experimental results on real-world data sets demonstrate the effectiveness for better cluster ensemble of the proposed method.

Hierarchical cluster ensemble model based on knowledge granulation

Accurate ROI localization and hierarchical hyper-sphere model for finger-vein recognition

Facial semantic descriptors based on information granules

Graphical abstractDisplay Omitted HighlightsProposed hybrid methodology is a coarse to fine approach to abdominal organs segmentation.The evolving organ contour is initialized by means of information granule.The image is fuzzified using Gaussian membership function.The granule-adjusted hybrid level set approach yields final segmentation.The approach effectiveness was confirmed based on 20 CT studies and 4 types of organs. Hybrid methodology that combines granular computing with a level set approach for image segmentation is introduced in this paper. The goal of the study is to provide an adjustable semi-automated method for 3D segmentation of abdominal organs in computed tomography studies. Based on initial guidance in terms of models or seed points the information granule is formulated for each anatomical structure under consideration. The granulation leads via spatial volume resampling and granule-driven image intensity fuzzification to the final segmentation stage employing a hybrid level set approach. Depending on the organ intensity and shape the segmentation runs with selected parameters. The algorithm has been evaluated by 20 computed tomography studies and four various structures delineated by an expert. The experimental part presents a comprehensive analysis of subsequent stages of the algorithm. The voxel-wise segmentation sensitivities reached 94.4% for the right kidney, 93.8% for the left kidney, 94.0% for the spleen, and 94.0% for the liver, with the Dice index at 93.7%, 94.2%, 91.0%, and 92.9%, respectively.

Calibrating level set approach by granular computing in computed tomography abdominal organs segmentation

Information granules in image histogram analysis.

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to formthe relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso.

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Image superresolution reconstruction via granular computing clustering

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Jun Huang

Papers

Image superresolution reconstruction via granular computing clustering

Fuzzy Cube Granule Structure for Image Segmentation