Improved Multi-scale Local Binary Pattern for Feature Extraction and Coral Reef Classification

Coral reefs are an important part of the tropical shallow water ecosystem and their protection is very important. Classification of coral reef images includes three stages of image enhancement, feature extraction and classification. In this research, by focusing on the feature extraction a method for features extraction for classification of coral corals images is proposed. This method consists of two methods of local binary pattern variants. In addition, instead of using a large neighborhoods, a multi-scale neighborhood with different sizes is used. This method employed a fixed number of points with different size of neighborhoods. This increases the classification accuracy without exponentially increasing the features. By combining the CS_LBP symmetric binary method and the MRELBP median enhanced binary method, some features of the image are merged together, and the local features extracted by the CS_LBP method are reduced by half in each step. In this research, the accuracy of the proposed model has been evaluated on EILAT, EILAT2, RSMAS, and MLC-2008 coral reef image sets. Also a general textures such as CUReT, UIUC, and KTH_TIPS texture are used. The classification accuracy of the proposed method has increased in all recent data, while the number of features extracted is decreased.