QUADRUPLET SELECTION METHODS FOR DEEP EMBEDDING LEARNING

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2019-01-01

Author

Karaman, Kaan
Gundogdu, Erhan
Koc, Aykut
Alatan, Abdullah Aydın

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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.

Subject Keywords

Deep distance metric learning, Embedding learning, Fine-grained classification/recognition

URI

https://hdl.handle.net/11511/43119

DOI

https://doi.org/10.1109/icip.2019.8803401

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

K. Karaman, E. Gundogdu, A. Koc, and A. A. Alatan, “QUADRUPLET SELECTION METHODS FOR DEEP EMBEDDING LEARNING,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43119.