Coral reef image classification employing Improved LDP for feature extraction

This paper presents a scheme for feature extraction that can be applied for classification of corals in submarine coral reef images. In coral reef image classification, texture features are extracted using the proposed Improved Local Derivative Pattern (ILDP). ILDP determines diagonal directional pattern features based on local derivative variations which can capture full information. For classification, three classifiers, namely Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN) with four distance metrices, namely Euclidean distance, Manhattan distance, Canberra distance and Chi-Square distance, and Support Vector Machine (SVM) with three kernel functions, namely Polynomial, Radial basis function, Sigmoid kernel are used. The accuracy of the proposed method is compared with Local Binary pattern (LBP), Local Tetra Pattern (LTrP), Local Derivative Pattern (LDP) and Robust Local Ternary Pattern (RLTP) on five coral data sets and four texture data sets. Experimental results indicate that ILDP feature extraction method when tested with five coral data sets, namely EILAT, RSMAS, EILAT2, MLC2012 and SDMRI and four texture data sets, namely KTH-TIPS, UIUCTEX, CURET and LAVA achieves the highest overall classification accuracy, minimum execution time when compared to the other methods.