QUADRUPLET SELECTION METHODS FOR DEEP EMBEDDING LEARNING


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Karaman K., Gundogdu E., Koc A., ALATAN A. A.

26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 22 - 25 September 2019, pp.3452-3456 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/icip.2019.8803401
  • City: Taipei
  • Country: Taiwan
  • Page Numbers: pp.3452-3456

Abstract

Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification. For discrimination of the objects in fine-grained detail, we focus on deep embedding learning by using a multi-task learning framework, in which the hierarchical labels (coarse and fine labels) of the samples are utilized both for classification and a quadruplet-based loss function. In order to improve the recognition strength of the learned features, we present a novel feature selection method specifically designed for four training samples of a quadruplet. By experiments, it is observed that the selection of very hard negative samples with relatively easy positive ones from the same coarse and fine classes significantly increases some performance metrics in a fine-grained dataset when compared to selecting the quadruplet samples randomly. The feature embedding learned by the proposed method achieves favorable performance against its state-of-the-art counterparts.