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Performance Comparison of Cosine, Haar, Walsh-Hadamard, Fourier and Wavelet Transform for shape based image retrieval using Fuzzy Similarity Measure

机译:基于模糊相似度量的基于形状的图像检索的余弦,哈尔,沃尔什哈拉德,傅立叶和小波变换的性能比较

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Shape is one of the most important features in Content Based Image Retrieval (CBIR). When a shape is used as feature, edge detection might be the first step of feature extraction. Invariance to the different transformations like translation, rotation, and scale is required by a good shape representation. In this paper a performance comparison is done on various image transforms like Wavelet transform, Fourier transform, Haar transform, Walsh-Hadamard transform and discrete cosine transform using a fuzzy similarity measure. It is seen that according to retrieval performance Wavelet transform gives the best result among the other mentioned transforms. It has higher recall and precision values and higher crossover point.
机译:形状是基于内容的图像检索(CBIR)中最重要的特征之一。 当形状用作特征时,边缘检测可能是特征提取的第一步。 通过良好的形状表示,需要不变于转换,旋转和比例等不同的变换。 在本文中,使用模糊相似度测量,在小波变换,傅立叶变换,HAAR变换,WALSH-HARAMARD变换和离散余弦变换等各种图像变换上进行性能比较。 可以看出,根据检索性能小波变换,在另一个提到的变换中提供了最佳结果。 它具有更高的召回和精度值和更高的交叉点。

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