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Region descriptors for automatic classification of small sea targets in infrared video

机译：红外视频中小海目标自动分类的区域描述符

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摘要

We evaluate the performance of different key-point detectors and region descriptors when used for automatic classification of small sea targets in infrared video. In our earlier research performed on this subject as well as in other literature, many different region descriptors have been proposed. However, it is unclear which methods are most applicable to use on the type of infrared imagery as used onboard naval ships. The key-point detector should detect points of interest that can be used to effectively describe the objects in the imagery. On the basis of the detected key points, the descriptors should discriminate between different classes of small sea targets while being robust to differences in viewing conditions. We propose a similarity measure based on the distance between key-point location and the Euclidean distance between descriptors to quantify the similarity of images. For performance evaluation, we use the receiver operator characteristic as the criterion to rank the evaluated methods. We compare the Harris-, blob- and scale-invariant feature transform (SIFT) detectors and the square neighborhood, steerable filters, invariant moments, and SIFT descriptors. We conclude that the Harris detector combined with the square neighborhood of size 19x 19 or the SIFT descriptor results in the best classification performance for our data set.
机译：当用于红外视频中小海目标的自动分类时，我们评估不同关键点检测器和区域描述符的性能。在我们对此主题的早期研究以及其他文献中，已经提出了许多不同的区域描述符。但是，目前尚不清楚哪种方法最适合用于舰船上的红外图像类型。关键点检测器应检测可用于有效描述图像中对象的兴趣点。基于检测到的关键点，描述符应区分不同类别的小海目标，同时对观察条件的差异具有鲁棒性。我们提出了一种基于关键点位置之间的距离和描述符之间的欧几里得距离的相似性度量，以量化图像的相似性。对于性能评估，我们以接收者的操作员特征为标准对评估方法进行排名。我们比较了哈里斯，斑点和尺度不变特征变换（SIFT）检测器以及平方邻域，可控滤波器，不变矩和SIFT描述符。我们得出结论，哈里斯检测器与19x 19大小的正方形邻域或SIFT描述符相结合，可以为我们的数据集带来最佳的分类性能。

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