Underwater sea cucumber identification based on Principal Component Analysis and Support Vector Machine

Underwater sea cucumber images are blurred and contain complex backgrounds. To improve the efficiency of sea cucumber identification, a method based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) was proposed. Firstly, colours, textures and shapes of the sample images were extracted. Then, each feature was used separately to train SVM to identify the target. These features were sorted by identification rate. PCA-SVM was used to train the classifier, and the classifier was proposed to identify sea cucumber images. The accuracy of our proposed method was 98.55%, the time taken was 0.73 s. These results were compared with those of Genetic Algorithm (GA)-SVM (97.10%, 19.50 s), Ant Colony Optimization (ACO)-SVM (94.20%, 228.72 s), and Artificial Neural Networks (ANN) (97.10%, 1.25 s). PCA-SVM had the highest accuracy and the shortest time. Thus, PCA-SVM as proposed herein could satisfy the requirement that an underwater robot rapidly and precisely identify sea cucumber objects in a real environment. (C) 2018 Published by Elsevier Ltd.