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

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 19×19 or the SIFT descriptor results in the best classification performance for our data set.Electrical Engineering, Mathematics and Computer Scienc