Coral reef dataset

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos.

本文提出一种适用于底栖珊瑚礁图像的新型图像分类方案，可同时适配单张图像与复合镶嵌数据集。所提方法可针对单个数据集的特征（如数据集规模、类别数量、样本分辨率、色彩信息可用性、类别类型等）进行适配配置。所提方法采用完备局部二值模式（Completed Local Binary Pattern, CLBP）、灰度共生矩阵（Grey Level Co-occurrence Matrix, GLCM）、Gabor滤波响应，以及对立角与色调通道颜色直方图作为特征描述符。分类阶段可选用k近邻（k-nearest neighbor, KNN）、神经网络（Neural Network, NN）、支持向量机（Support Vector Machine, SVM）或概率密度加权平均距离（Probability Density Weighted Mean Distance, PDWMD）作为分类器。本文同时给出了可获得最优分类效果的特征与分类器组合方案，以及对应的选择指南。本文采用3个底栖数据集与3个纹理数据集，将所提方法的分类精度与运行效率与其他当前最优技术进行对比。在所测试的所有方法中，所提方法的总体分类精度最高，且运行时长处于适中水平。最后，本文将所提分类方案应用于红海的大规模图像镶嵌数据，生成了一套完整的珊瑚礁底栖生物分类专题图。