Discrete Multi-view Hashing for Effective Image Retrieval
Discrete Multi-view Hashing for Effective Image Retrieval
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DOI：
10.1145/3078971.3078981
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Rui Yang;Yuliang Shi;Xin-Shun Xu
中科院分区：
文献类型：
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作者：
Rui Yang;Yuliang Shi;Xin-Shun Xu
最近，散列技术由于其低存储成本和用于大规模数据检索任务（例如，图像检索已经提出了许多方法;然而，大多数现有的散列技术集中在单视图数据上。在许多情况下，数据样本中有多个视图。因此，这些方法工作在单视图不能充分利用多视图数据中包含的丰富信息。虽然已经提出了一些针对多视图数据的方法，但它们通常放松二进制约束或将哈希函数和二进制代码的学习过程分成两个独立的阶段，以绕过处理二进制代码的离散约束进行优化的障碍，这可能会产生较大的量化误差。为了解决这些问题，本文提出了一种新的哈希方法，离散多视图散列算法（DMVH）可以直接处理多视图数据，充分利用多视图数据中的丰富信息。此外，在DMVH中，我们直接优化离散码，而不是放松二进制约束，使我们可以获得高质量的哈希码。同时，提出了一种新的相似度矩阵构造方法，该方法既能保持数据点之间的局部相似性结构，又能保持数据点之间的语义相似性。为了解决DMVH中的优化问题，我们进一步提出了一种替代算法。我们在三个大规模的数据集上测试了所提出的模型。实验结果表明，它优于或相当于几个国家的艺术。
Recently, hashing techniques have witnessed an increase in popularity due to their low storage cost and high query speed for large scale data retrieval task, e.g., image retrieval. Many methods have been proposed; however, most existing hashing techniques focus on single view data. In many scenarios, there are multiple views in data samples. Thus, those methods working on single view can not make full use of rich information contained in multi-view data. Although some methods have been proposed for multi-view data; they usually relax binary constraints or separate the process of learning hash functions and binary codes into two independent stages to bypass the obstacle of handling the discrete constraints on binary codes for optimization, which may generate large quantization error. To consider these problems, in this paper, we propose a novel hashing method, i.e., Discrete Multi-view Hashing (DMVH), which can work on multi-view data directly and make full use of rich information in multi-view data. Moreover, in DMVH, we optimize discrete codes directly instead of relaxing the binary constraints so that we could obtain high-quality hash codes. Simultaneously, we present a novel approach to construct similarity matrix, which can not only preserve local similarity structure, but also keep semantic similarity between data points. To solve the optimization problem in DMVH, we further propose an alternate algorithm. We test the proposed model on three large scale data sets. Experimental results show that it outperforms or is comparable to several state-of-the-arts.