作 者：
张重阳;陈明;冯国富;郭强;周旭;侍国忠;陈冠奇
关键词：
鱼群摄食行为;图像处理;多特征融合;机器学习;BP网络
摘 要：
针对目前检测方法特征单一、样本数量少和鲁棒性低等问题,提出了一种基于多特征融合与机器学习的鱼类摄食行为的检测方法:利用图像处理技术提取鱼群摄食图像的颜色、形状和纹理特征,并对其进行归一化和特征融合处理,通过构建3层的BP神经网络对鱼群摄食行为进行检测。与SVM和KNN检测效果进行对比,BP神经网络的效果最好,精度可达97.1%。与传统的基于单一纹理特征方法相比,在保证时效性和增强鲁棒性的同时,准确率提高了4.1%。
译 名：
Detection method of fish feeding behavior based onthe multi-feature fusion and the machine learning
作 者：
ZAHNG Chongyang;CHEN Ming;FENG Guofu;GUO Qiang;ZHOU Xu;SHI Guozhong;CHEN Guanqi;College of Information Technology,Shanghai Ocean University;Key Laboratory ofFisheries Information,Ministry of Agriculture;
单 位：
ZAHNG Chongyang%CHEN Ming%FENG Guofu%GUO Qiang%ZHOU Xu%SHI Guozhong%CHEN Guanqi%College of Information Technology,Shanghai Ocean University%Key Laboratory ofFisheries Information,Ministry of Agriculture
关键词：
fish feeding behavior;;image processing;;multi-feature fusion;;machine learning;;BP network
摘 要：
Aiming at the problems of single feature, less data and low robustness of current detection methods for the fish feeding behavior, a detection method was proposed based on the multi-feature fusion and the machine learning, by using image processing technology to extract the color, shape and texture features of the fish feeding images, which were processed by normalization and feature fusion. The fish feeding behavior is checked by constructing a 3-layer BP neural network. The results show that compared with SVM and KNN, the BP neural network has the best effect, and the accuracy can reach 97.1%. Compared with the traditional method based on the single texture feature, the accuracy is improved by 4.1% while guaranteeing timeliness and enhancing robustness.
