仿射变换下曲率尺度空间(CSS)技术和自组织映射(SOM)模型的图像检索

C. Almeida, R. Souza, C. Rodrigues, N. L. C. Junior
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引用次数: 1

摘要

在之前的工作[1]中,我们提出了一种使用曲率尺度空间(CSS)和自组织映射(SOM)方法的基于形状的图像检索方法。在这里,我们研究了在仿射变换下表示的鲁棒性。此外,从数据库中提取的CSS图像通过中值向量进行处理和描述,这些中值向量构成了SOM神经网络的训练数据集。与先前使用主成分分析技术的第一主成分的工作[1]相比,这种描述方式提高了图像检索的准确性。在一个基准数据库上进行了实验,以证明所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Retrieval Using the Curvature Scale Space (CSS) Technique and the Self-Organizing Map (SOM) Model under Affine Transforms
In a previous work [1], we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation under affine transforms. Moreover, the CSS images extracted from a database are processed and described by median vectors that constitutes the training data set for a SOM neural network. This way of description improves the accuracy of image retrieval in comparison with the previous work [1] that used the first principal component of the PCA technique. Experiments with a benchmark database are carried out to demonstrate the usefulness of the proposed methodology.
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