Bottom sediment classification method from seabed image for automatic counting system of scallop

Abstract

Each related organization conducts various fishery investigations and collects data required for estimation of re source state. In the scallop culture industry in To koro, Japan, the fish resources are investigated by analyzing seabed images. The seabed images are now obtainable from catamaran technology. However, there is no automatic technology to measure data from these images, and so the current investigation technique is the manual measurement by experts. The scallop is looked different from each environment. Therefore, a suitable algorithm to extract the scallop area depends on the bottom sediments of the seabed image. In this paper, we propose a method to classify the bottom sediments of the seabed image. For bottom sediment classification, we forge a strong classifier from weak classifiers using AdaBoost using the various texture features. This paper describes a method to classify the bottom sediments, presents the comparison of the effectiveness of the texture features and the results. Moreover, we presents the experiments results of the scallop counting based on the proposed method, and evaluate the method's effectiveness.