An image can be represented by different types of feature descriptors. Each descriptor holds specific details about the image. The image classification and retrieval applications use these extracted features to classify the query image and retrieve similar kinds of images presented in huge databases for the given query image. The discriminant feature in the form of a binary pattern available around each pixel of the image is extracted by taking the local pixel difference between each pixel and its neighboring pixels (i.e., sampling points) present in different radii. The discrete wavelet representation (i.e., multi-resolution) of the image has more information about the image compared to the image present in the spatial domain since each level of the multi-level decomposition discloses significant details about the image in separate channels. However, binary pattern extraction around each coefficient of the different levels of decomposed images lacks in producing the discriminant texture feature representation since it extracts a feature from each decomposition level at a time and concatenates them. Thus, the proposed work extracts the binary pattern around each coefficient of different levels of decomposed image from different radii. Then, the obtained binary patterns available in different decomposition levels are encoded to represent the discriminant texture feature around each pixel location. Consequently, the proposed work selects the multi-level decomposition based on the stationary wavelet transform, since it has the same resolution in each level of decomposition. The performance of the proposed feature descriptor is assessed over seven different sets of databases using image classification and image retrieval applications. Finally, the performance of the proposed work is compared with the state-of-the-art techniques involved in extracting spatial arrangement details of pixels available in the image.

中文翻译：

---

一种新的多级径向差分编码模式，用于图像分类和检索

图像可以由不同类型的特征描述符表示。每个描述符都包含有关图像的特定详细信息。图像分类和检索应用程序使用这些提取的功能对查询图像进行分类，并检索给定查询图像在大型数据库中呈现的相似类型的图像。通过获取每个像素与其以不同半径存在的相邻像素（即采样点）之间的局部像素差，可以提取图像每个像素周围可用的二进制模式形式的判别特征。与空间域中存在的图像相比，图像的离散小波表示（即多分辨率）具有有关图像的更多信息，因为多级分解的每个级别都在单独的通道中公开了有关图像的重要细节。然而，围绕分解图像的不同级别的每个系数的二进制图案提取在产生可辨别的纹理特征表示方面是缺乏的，因为它一次从每个分解级别提取特征并将其连接。因此，拟议的工作从不同的半径提取不同级别的分解图像周围的每个系数的二进制模式。然后，对获得的可用于不同分解级别的二进制模式进行编码，以表示每个像素位置周围的判别纹理特征。因此，由于在每个分解级别上具有相同的分辨率，因此建议的工作基于平稳小波变换选择了多级分解。使用图像分类和图像检索应用程序，通过七个不同的数据库集评估提出的特征描述符的性能。最后，将拟议工作的性能与提取图像中可用像素的空间布置细节所涉及的最新技术进行比较。