Quadruplet Selection Methods for Deep Embedding Learning

Kaan Karaman; Erhan Gundogdu; Aykut Koc; A. Aydin Alatan

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arXiv · 1907.09245

Quadruplet Selection Methods for Deep Embedding Learning

Kaan Karaman·Erhan Gundogdu·Aykut Koc·A. Aydin Alatan

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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.

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BibTeX RIS

Kaan Karaman, Erhan Gundogdu, Aykut Koc, A. Aydin Alatan. 2019-07-22. Quadruplet Selection Methods for Deep Embedding Learning. https://arxiv.org/abs/1907.09245

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