Metric learning using deep recurrent networks for visual clustering and retrieval

Can, Oğul
Learning an image similarity metric plays a key role in visual analysis, especially for the cases where a training set contains a large number of hard negative samples that are difficult to distinguish from other classes. Due to the outstanding results of the deep metric learning on visual tasks, such as image clustering and retrieval, selecting a proper loss function and a sampling method becomes a central issue to boost the performance. The existing metric learning approaches have two significant drawbacks; inadequate mini-batch sampling and disregarding higher-order relations between data samples. In this thesis, two novel methods are proposed to alleviate these deficiencies. At first, a novel loss function is introduced to identify multiple similar examples in a local neighborhood. Moreover, a novel batch construction method is presented to select representative hard negatives. The training of a deep network is achieved by using this novel cost function through the proposed batch construction approach. In order to consider higher-order relations between samples, a novel deep metric learning framework that contains recurrent neural networks architecture is proposed. Extensive experimental results on three publicly available datasets show that proposed approaches yield competitive or better performance in comparison with state-of-the-art metric learning methods.


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Citation Formats
O. Can, “Metric learning using deep recurrent networks for visual clustering and retrieval,” M.S. - Master of Science, Middle East Technical University, 2018.