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On the Existence of Kernel Function for Kernel-Trick of k-Means in the Light of Gower Theorem

100%

Kłopotek M. A.

tom Vol. 168, nr 1

25--43

This paper, constituting an extension to the conference paper [1], corrects the proof of the Theorem 2 from the Gower’s paper [2, page 5]. The correction is needed in order to establish the existence of the kernel function used commonly in the kernel trick e.g. for k-means clustering algorithm, on the grounds of distance matrix. The correction encompasses the missing if-part proof and dropping unnecessary conditions.

Detection of the change point in the winter wheat experiment

100%

Szczepańska A. , Bakinowska E.

tom 49

nr 1

37-44

This paper concerns detection of the change point, which is treated as an abrupt change in the response function or one of its derivatives. The change point is identified using the semiparametric model and the theory given by Speckman (1994). The theory is illustrated by a real experiment in wchich the dry biomass of winter wheat is studied.

A Robust Multiple Classifier System for Pixel Classification of Remote Sensing Images

86%

Maulik U. , Chakraborty D.

tom Vol. 101, nr 4

286-304

Satellite image classification is a complex process that may be affected by many factors. This article addresses the problem of pixel classification of satellite images by a robust multiple classifier system that combines k-NN, support vector machine (SVM) and incremental learning algorithm (IL). The effectiveness of this combination is investigated for satellite imagery which usually have overlapping class boundaries. These classifiers are initially designed using a small set of labeled points. Combination of these algorithms has been done based on majority voting rule. The effectiveness of the proposed technique is first demonstrated for a numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on numeric data as well as two remote sensing data show that employing combination of classifiers can effectively increase the accuracy label. Comparison is made with each of these single classifiers in terms of kappa value, accuracy, cluster quality indices and visual quality of the classified images.

A hybrid approach for the delineation of brain lesion from CT images

72%

Gautam A. , Raman B. , Raghuvanshi S.

tom Vol. 38, no. 3

504--518

Brain lesion segmentation from radiological images is the most important task in accurate diagnosis of patients. This paper presents a hybrid approach for the segmentation of brain lesion from computed tomography (CT) images based on the combination of fuzzy clustering using hyper tangent function as the robust kernel and distance regularized level set evolution (DRLSE) function as the edge based active contour method. Kernel based fuzzy clustering method divides the image into different regions. These regions can be used to find region of interest by using DRLSE algorithm to generate the optimal region boundary. The proposed method results in smooth boundary of the required regions with high accuracy of segmentation. In this paper, results are compared with standard fuzzy c-means (FCM) clustering, spatial FCM, robust kernel based fuzzy clustering (RFCM) and DRLSE algorithms. The performance of the proposed method is evaluated on CT scan images of hemorrhagic lesion, which shows that our method can segment brain lesion more accurately than the other conventional methods.