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Texture Image Retrieval Based on Gray Level Co-Occurrence Matrix and Singular Value Decomposition

机译:基于灰度共生矩阵和奇异值分解的纹理图像检索

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This paper proposed a novel method, which uses gray level co-occurrence matrix (GLCM) and singular value decomposition (SVD) to extract the features of texture images for image retrieval. In our algorithm, we take the top ten singular values which are based on SVD as the feature vectors, and then uses the Euclidean distance for similarity measure. Compared with traditional GLCM-based feature extraction methods (such as the mean and variance), our new method can improve the retrieval efficiency. Experiments show that the proposed approach is satisfactory and achieves better retrieval performance.
机译:本文提出了一种新的方法,该方法利用灰度共生矩阵(GLCM)和奇异值分解(SVD)来提取纹理图像的特征以进行图像检索。在我们的算法中,我们将基于SVD的前十个奇异值用作特征向量,然后使用欧几里得距离进行相似性度量。与传统的基于GLCM的特征提取方法(例如均值和方差)相比,我们的新方法可以提高检索效率。实验表明,该方法是令人满意的,并取得了较好的检索性能。

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