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Deep Spherical Quantization for Image Search

2019-06-07 02:21:16
Sepehr Eghbali, Ladan Tahvildari

Abstract

Hashing methods, which encode high-dimensional images with compact discrete codes, have been widely applied to enhance large-scale image retrieval. In this paper, we put forward Deep Spherical Quantization (DSQ), a novel method to make deep convolutional neural networks generate supervised and compact binary codes for efficient image search. Our approach simultaneously learns a mapping that transforms the input images into a low-dimensional discriminative space, and quantizes the transformed data points using multi-codebook quantization. To eliminate the negative effect of norm variance on codebook learning, we force the network to L_2 normalize the extracted features and then quantize the resulting vectors using a new supervised quantization technique specifically designed for points lying on a unit hypersphere. Furthermore, we introduce an easy-to-implement extension of our quantization technique that enforces sparsity on the codebooks. Extensive experiments demonstrate that DSQ and its sparse variant can generate semantically separable compact binary codes outperforming many state-of-the-art image retrieval methods on three benchmarks.

Abstract (translated)

散列法是用紧凑的离散码对高维图像进行编码的一种方法,在大规模图像检索中得到了广泛的应用。本文提出了一种使深卷积神经网络产生有监督的、紧凑的二值编码的有效图像搜索方法——深球面量化(DSQ)。我们的方法同时学习将输入图像转换为低维识别空间的映射,并使用多码本量化对转换后的数据点进行量化。为了消除范数方差对码书学习的负面影响,我们强制网络对提取的特征进行L_2规范化,然后使用一种新的监督量化技术对结果矢量进行量化,该技术专门针对单位超球面上的点设计。此外,我们还介绍了量化技术的一个易于实现的扩展,它强制代码簿上的稀疏性。大量实验表明,DSQ及其稀疏变量可以生成语义上可分离的紧凑二进制码,这在三个基准上优于许多最先进的图像检索方法。

URL

https://arxiv.org/abs/1906.02865

PDF

https://arxiv.org/pdf/1906.02865.pdf


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