Attribute hashing for zero-shot image retrieval
Attribute hashing for zero-shot image retrieval
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DOI：
10.1109/icme.2017.8019425
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Yahui Xu;Yang Yang-Yang;Fumin Shen;Xing Xu;Yuxuan Zhou-;Heng Tao Shen
中科院分区：
文献类型：
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作者：
Yahui Xu;Yang Yang-Yang;Fumin Shen;Xing Xu;Yuxuan Zhou-;Heng Tao Shen
哈希算法以其高效性和有效性被认为是高维数据索引和检索的最有前途的方法之一。然而，大多数现有的方法不可避免地遭受“语义鸿沟”的问题，特别是当面对快速发展的新出现的“看不见的”类别在Web上。在这项工作中，我们提出了一种创新的方法，称为属性哈希（AH），以促进零拍摄图像检索（即，通过“看不见的”图像查询）。特别是，我们提出了一个多层层次的哈希，充分利用属性来模拟视觉特征，二进制代码和标签之间的关系。此外，我们有意保留哈希码的性质（即，（3）局部结构和局部结构（最大程度）。我们在几个真实世界的图像数据集上进行了广泛的实验，以显示我们提出的AH方法与最先进的方法相比的优越性。
Hashing has been recognized as one of the most promising ways in indexing and retrieving high-dimensional data due to the excellent merits in efficiency and effectiveness. Nevertheless, most existing approaches inevitably suffer from the problem of “semantic gap”, especially when facing the rapid evolution of newly-emerging “unseen” categories on the Web. In this work, we propose an innovative approach, termed Attribute Hashing (AH), to facilitate zero-shot image retrieval (i.e., query by “unseen” images). In particular, we propose a multi-layer hierarchy for hashing, which fully exploits attributes to model the relationships among visual features, binary codes and labels. Besides, we deliberately preserve the nature of hash codes (i.e., discreteness and local structure) to the greatest extent. We conduct extensive experiments on several real-world image datasets to show the superiority of our proposed AH approach as compared to the state-of-the-arts.