  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.
