Deep Triplet Quantization
This work addresses image retrieval efficiency for applications requiring compact binary codes, but it is incremental as it builds on existing deep learning to quantization approaches.
The authors tackled the problem of efficient image retrieval by proposing Deep Triplet Quantization (DTQ), a method that learns deep quantization models from similarity triplets, resulting in state-of-the-art performance on benchmark datasets like NUS-WIDE, CIFAR-10, and MS-COCO.
Deep hashing establishes efficient and effective image retrieval by end-to-end learning of deep representations and hash codes from similarity data. We present a compact coding solution, focusing on deep learning to quantization approach that has shown superior performance over hashing solutions for similarity retrieval. We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets. To enable more effective triplet training, we design a new triplet selection approach, Group Hard, that randomly selects hard triplets in each image group. To generate compact binary codes, we further apply a triplet quantization with weak orthogonality during triplet training. The quantization loss reduces the codebook redundancy and enhances the quantizability of deep representations through back-propagation. Extensive experiments demonstrate that DTQ can generate high-quality and compact binary codes, which yields state-of-the-art image retrieval performance on three benchmark datasets, NUS-WIDE, CIFAR-10, and MS-COCO.