论文标题
锚图结构融合散列用于跨模式相似性搜索
Anchor Graph Structure Fusion Hashing for Cross-Modal Similarity Search
论文作者
论文摘要
跨模式哈希仍然存在一些解决的挑战：（1）大多数现有的CMH方法将图形作为模型数据分布的输入。这些方法忽略了多种方式之间图形结构的相关性。 （2）考虑到多模式数据之间的融合亲和力，大多数现有的CMH方法忽略了； （3）大多数现有的CMH方法放宽了离散约束以解决优化目标，从而大大降低了检索性能。为了解决上述局限性，我们提出了一种新颖的锚图结构融合散列（AGSFH）。 AGSFH与Hadamard乘积从多种模态的不同锚图中构造了锚图结构融合矩阵，这可以充分利用基础数据结构的几何特性。基于锚图结构融合矩阵，AGSFH试图直接学习固有的锚图，其中固有锚图的结构被自适应调节，因此固有图的组件数量完全等于簇数。此外，AGSFH还将锚融合亲和力保留到普通的二元锤子空间中。此外，一个离散的优化框架旨在学习统一的二进制代码。三个公共社交数据集的广泛实验结果证明了AGSFH的优势。
Cross-modal hashing still has some challenges needed to address: (1) most existing CMH methods take graphs as input to model data distribution. These methods omit to consider the correlation of graph structure among multiple modalities; (2) most existing CMH methods ignores considering the fusion affinity among multi-modalities data; (3) most existing CMH methods relax the discrete constraints to solve the optimization objective, significantly degrading the retrieval performance. To solve the above limitations, we propose a novel Anchor Graph Structure Fusion Hashing (AGSFH). AGSFH constructs the anchor graph structure fusion matrix from different anchor graphs of multiple modalities with the Hadamard product, which can fully exploit the geometric property of underlying data structure. Based on the anchor graph structure fusion matrix, AGSFH attempts to directly learn an intrinsic anchor graph, where the structure of the intrinsic anchor graph is adaptively tuned so that the number of components of the intrinsic graph is exactly equal to the number of clusters. Besides, AGSFH preserves the anchor fusion affinity into the common binary Hamming space. Furthermore, a discrete optimization framework is designed to learn the unified binary codes. Extensive experimental results on three public social datasets demonstrate the superiority of AGSFH.
