自动化学报2016，Vol.42Issue(6)：875-882,8.DOI:10.16383/j.aas.2016.c150741
一种基于CLMF的深度卷积神经网络模型
Convolutional Neural Networks with Candidate Location and Multi-feature Fusion
摘要
Abstract
To solve the problem that the traditional manual feature extraction models are unable to satisfy object recognition in complex environment, an object recognition model based on convolutional neural networks with candidate location and multi-feature fusion (CLMF-CNN) model is proposed. The model combines the visual saliency, multi-feature fusion and CNN model to realize the object recognition. Firstly, the candidate objects are conformed via weighted Itti model. Consequently, color and intensity features are obtained via CNN model respectively. After the multi-feature fusion method, the features can be used for object recognition. Finally, the model is tested and compared with the single feature method and current popular algorithms. Experimental result in this paper proves that our method can not only get good performance in improving the accuracy of object recognition, but also satisfy real-time requirements.关键词
图像识别/深度学习/卷积神经网络/多特征融合Key words
Image recognition/deep learning/convolutional neural networks (CNN)/multi-feature fusion引用本文复制引用
随婷婷,王晓峰..一种基于CLMF的深度卷积神经网络模型[J].自动化学报,2016,42(6):875-882,8.基金项目
国家自然科学基金(31170952),国家海洋局项目(201305026),上海海事大学优秀博士学位论文培育项目(2014bxlp005),上海海事大学研究生创新基金项目(2014ycx047)资助Supported by National Natural Science Foundation of China (31170952), Foundation of the National Bureau of Oceanogra-phy (201305026), Excellent Doctoral Dissertation Cultivation Foundation of Shanghai Maritime University (2014bxlp005), and Graduate Innovation Foundation of Shanghai Maritime Univer-sity (2014ycx047) （31170952）
