This paper presents a content-based image retrieval technique that focuses on extraction and reduction in multiple features. To obtain multi-level decomposition of the image by extracting approximation and correct coefficients, discrete wavelet transformation is applied to the RGB channels initially. Therefore, both approximation and correct coefficients are applied to the dominant rotated local binary pattern termed as texture descriptor which is computationally effective and rotationally invariant. For a local neighbor patch, a rotation invariance function image is obtained by measuring the descriptor relative to the reference. The proposed approach contains the complete structural information extracted from the local binary patterns and also extracts the additional information using the information of magnitude, thereby achieving extra discriminative power. Then, GLCM description is used by obtaining the dominant rotated local binary pattern image to extract the statistical characteristics for texture image classification. The proposed technique is applied to CORAL dataset with the help of particle swarm optimization-based feature selector to minimize the number of features that can be used during the classification process. The three classifiers, i.e., support vector machine, K-nearest neighbor, and decision tree, are trained and tested. The comparison is based in terms of Accuracy, precision, recall, and F-measure performance metrics for classification. Experimental results show that the proposed approach achieves better accuracy, precision, recall, and F-measure values for most of the CORAL dataset classes.

本文提出了一种基于内容的图像检索技术，该技术着重于多种特征的提取和归约。为了通过提取近似值和正确系数来获得图像的多级分解，将离散小波变换应用于RGB最初的渠道。因此，将近似系数和正确系数都应用于被称为纹理描述符的占优势的旋转局部二进制图案，该图案在计算上是有效的并且在旋转上不变。对于局部邻居补丁，通过测量相对于参考的描述符来获得旋转不变性函数图像。所提出的方法包含从局部二进制模式提取的完整结构信息，还使用幅度信息提取附加信息，从而获得额外的判别能力。然后，通过获得主要旋转局部二值模式图像来使用GLCM描述来提取统计特征以进行纹理图像分类。提出的技术应用于珊瑚数据集借助基于粒子群优化的特征选择器来最大程度地减少分类过程中可以使用的特征数量。训练和测试了三个分类器，即支持向量机，K近邻和决策树。该比较基于准确性，精度，召回率和用于分类的F量度性能指标。实验结果表明，对于大多数CORAL数据集类别，该方法均能获得更好的准确性，精度，召回率和F度量值。