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Image texture classification and retrieval using self-organizing map

机译:使用自组织图进行图像纹理分类和检索

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Nowadays there has been great interest in field of image texture classification and retrieval. The increasing use of digital images has increased the size of image database which resulted in the need to develop a system that will classify and retrieve the required image of interest efficiently and accurately. This paper presents an effective and accurate method to classify and retrieve image using Self-organizing maps (SOM). The proposed method employs two phases, in the first phase color histogram is used to extract the color features and then the extracted features are given to Self-organizing map for initial classification. In the second phase Gray level co-occurrence matrix (GLCM) is used to extract the texture information from all images in each class from initial classification and then again given to Self-organizing map for final classification. The experimental results show the efficiency of the proposed method.
机译:如今,对图像纹理分类和检索的领域非常兴趣。越来越多的数字图像的使用增加了图像数据库的大小,这导致需要开发一个系统,该系统可以有效且准确地对其进行分类和检索所需的感兴趣图像。本文介绍了使用自组织地图(SOM)对图像进行分类和检索图像的有效和准确的方法。所提出的方法采用两个阶段,在第一相色直方图中用于提取颜色特征,然后给出提取的特征以进行自组织地图以进行初始分类。在第二相位灰度级共发生矩阵(GLCM)用于从初始分类中从每个类中的所有图像中提取纹理信息,然后再次给予自组织地图以进行最终分类。实验结果表明了该方法的效率。

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