基于深度特征包的船舶自动分类
Automatic Ship Classification Utilizing Bag of Deep Features
摘要：
基于自然图像中船舶轮廓的船舶检测与分类是计算机科学的重要研究课题。这个问题可以从不同的角度来看，包括安全，交通管制，甚至军国主义。因此，在上述每个应用中，需要特定的处理。本文通过应用“词袋”（BoW），提出了一种新的方法，其词是使用深度卷积网络的预训练模型获得的特征。利用三个VGG模型，其在识别对象时提供了优异的准确性。选择作为初始建议的图像区域是从由尺度不变特征变换（SIFT）方法生成的关键点上的贪心算法导出的。在BOW方法中引入深度特征，对船舶的识别和分类起到了很好的改善作用。最后，我们得到了91.8%的准确率在船舶的分类，这表明约5%的改善相比，以前的方法。
英文摘要：
Detection and classification of ships based on their silhouette profiles in natural imagery is an important undertaking in computer science. This problem can be viewed from a variety of perspectives, including security, traffic control, and even militarism. Therefore, in each of the aforementioned applications, specific processing is required. In this paper, by applying the "bag of words" (BoW), a new method is presented that its words are the features that are obtained using pre-trained models of deep convolutional networks. , Three VGG models are utilized which provide superior accuracy in identifying objects. The regions of the image that are selected as the initial proposals are derived from a greedy algorithm on the key points generated by the Scale Invariant Feature Transform (SIFT) method. Using the deep features in the BOW method provides a good improvement in the recognition and classification of ships. Eventually, we obtained an accuracy of 91.8% in the classification of the ships which shows the improvement of about 5% compared to previous methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
