An Improved Ship Recognition Method Based on Bag-of-Visual-Words
Ship identification plays a critical role in maritime trade and military activities.Current research largely relies on deep learning-based methods,which demand extensive datasets and high-end hardware,often necessitating GPUs.This requirement significantly limits their practical application.Addressing this challenge,this paper introduces an enhanced bag-of-visual-words(BoVW)model based on classical computer vision techniques for rapid ship identification.The proposed method initially employs SIFT and SURF techniques to extract local features from ship images,followed by rapid matching and fusion of these features.A graph-theoretic approach is then used to determine the regions of interest(ROI)within the image,reducing background interference.Subsequently,clustering algorithms transform features within the ROIs into visual words and construct a visual dictionary.Each image is described using histograms of visual words.The method also employs a spatial pyramid kernel to represent spatial relationships between image features and uses support vector machines(SVM)for supervised learning classification.Key parameters in the model include the size of the visual dictionary and the resolution level.Extensive experiments were conducted to explore these parameters.When the visual dictionary size was set to 300 and the resolution level to 2,the model achieved an accuracy and precision exceeding 96％,validating the effectiveness of the proposed method.
bag of visual wordslocal featurefeature matchingship imagerecognition
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