Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
Ship Recognition Based on Active Learning and Composite Kernel SVM
verfasst von : Bin Pan, Zhiguo Jiang, Junfeng Wu, Haopeng Zhang, Penghao Luo
Erschienen in: Advances in Image and Graphics Technologies
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Aiming at recognizing ship target efficiently and accurately, a novel method based on active learning and the Composite Kernel Support Vector Machines (CK-SVM) is proposed. First, we build a ship recognition dataset which contains the major warship models and massive civil ships. Second, to reduce the cost of manual labeling, active learning algorithm is run to select the most informative and representative samples to label. Finally, we construct a composite-kernel SVM combining shape and texture features to recognize ships. The composite-kernel strategy can enhance the quality of features fusion apparently. Experiments demonstrate that our method not only improves the efficiency of samples selection, but also receives satisfying results.