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Manifold-ranking embedded order preserving hashing for image semantic retrieval

机译：用于图像语义检索的流形排序嵌入顺序保留哈希

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摘要

Due to the storage and computational efficiency of hashing technology, it has proven a valuable tool for large scale similarity search. In many cases, the large scale data in real-world lie near some (unknown) low-dimensional and non-linear manifold. Moreover, Manifold Ranking approach can preserve the global topological structure of the data set more effectively than Euclidean Distance-based Ranking approach, which fails to preserve the semantic relevance degree. However, most existing hashing methods ignore the global topological structure of the data set. The key issue is how to incorporate the global topological structure of data set into learning effective hashing function. In this paper, we propose a novel unsupervised hashing approach, namely Manifold-Ranking Embedded Order Preserving Hashing (MREOPH). A manifold ranking loss is introduced to solve the issue of global topological structure preserving. An order preserving loss is introduced to ensure the consistency between manifold ranking and hamming ranking. A hypercubic quantization loss is introduced to learn discrete binary codes. The information theoretic regularization term is taken into consideration for preserving desirable properties of hash codes. Finally, we integrate them in a joint optimization framework for minimizing the information loss in each processing. Experimental results on three datasets for semantic search clearly demonstrate the effectiveness of the proposed method.(C) 2017 Elsevier Inc. All rights reserved.
机译：由于哈希技术的存储和计算效率，它已被证明是用于大规模相似性搜索的有价值的工具。在许多情况下，现实世界中的大规模数据都位于一些（未知）低维和非线性流形附近。此外，与基于欧氏距离的排名方法相比，流形排序方法可以更有效地保留数据集的全局拓扑结构，后者无法保留语义相关度。但是，大多数现有的哈希方法都忽略了数据集的全局拓扑结构。关键问题是如何将数据集的全局拓扑结构纳入学习有效的哈希函数中。在本文中，我们提出了一种新颖的无监督哈希方法，即流形排名嵌入式订单保留哈希（MREOPH）。为了解决全局拓扑结构保留问题，引入了流形排序损失。为了保证流形等级和汉明等级之间的一致性，引入了保序损失。引入超三次量化损失以学习离散二进制代码。为了保留哈希码的期望特性，考虑了信息理论正则化项。最后，我们将它们集成在联合优化框架中，以最大程度地减少每次处理中的信息丢失。在三个用于语义搜索的数据集上的实验结果清楚地证明了该方法的有效性。（C）2017 Elsevier Inc.保留所有权利。

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