Line Segment-Based Clustering Approach With Self-Organizing Maps

G. Chamundeswari, G. Varma, C. Satyanarayana
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Abstract

Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.
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基于线段的自组织映射聚类方法
聚类技术在计算机视觉和模式识别中有着广泛的应用。发现聚类技术对输入图像的特征向量是有效的。因此,本文采用了一种基于霍夫变换的特征向量评估方法。利用霍夫变换,将点映射到线段上。将直线特征作为特征向量,交给神经网络进行聚类。本文采用自组织映射(SOM)神经网络进行聚类处理。用不同的叶片图像对该方法进行了评价,评价结果表明了该方法的有效性。
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