Hypergraph-based multi-example ranking with sparse representation for transductive learning image retrieval
Hypergraph-based multi-example ranking with sparse representation for transductive learning image retrieval
复制标题
基于超图的稀疏表示多示例排序用于转导学习图像检索
DOI：
10.1016/j.neucom.2012.09.001
复制
发表时间：
2013-02-04
期刊：
影响因子：
6
通讯作者：
Zhu, Jianke
中科院分区：
文献类型：
--
作者：
Hong, Chaoqun;Zhu, Jianke
基于内容的图像检索（CBIR）始终受到所谓的语义鸿沟的困扰。引入多个示例查询（QBME）来桥接它并在许多 CBIR 系统中得到应用。然而，当前的QBME方法通常分别对每个示例进行查询，然后组合查询结果。这样，计算时间将随着查询示例数量的增加而线性增加。在本文中，我们提出了一种基于转导学习框架的快速图像检索 QBME 方法。为了提高 QBME 的速度，我们引入了两项改进。首先，我们探索训练过程中图像数据的语义相关性。这些相关性是使用稀疏表示来学习的。利用语义相关性，构建语义相关性超图（SCHG）来对图像及其相关性进行建模。 SCHG 的构建没有任何参数。构建 SCHG 后，我们利用预先学习的语义相关性来预测图像的排名值。其次，我们提出了一种多重探测策略来对具有多个查询示例的图像进行排名。与一次接受一个输入示例的传统 QBME 方法不同，该策略中同时处理所有输入示例。实验结果证明了该方法在检索性能和速度上的有效性。 (C) 2012 Elsevier B.V. 保留所有权利。
Content-based image retrieval (CBIR) always suffers from the so-called semantic gap. Query-By-Multiple-Examples (QBME) is introduced to bridge it and applied in a lot of CBIR systems. However, current QBME methods usually query with each example separately and combine the query results. In this way, the computational time will increase linearly with the growing number of query examples. In this paper, we propose a novel QBME method for fast image retrieval based on transductive learning framework. To improve the speed of QBME, we introduce two improvements. First, we explore the semantic correlations of image data in the training process. These correlations are learned using sparse representation. With the semantic correlations, semantic correlation hypergraph (SCHG) is constructed to model the images and their correlations. The construction of SCHG is free of any parameter. After constructing SCHG, we predict the ranking values of images by using the pre-learned semantic correlations. Second, we propose a multiple probing strategy to rank the images with multiple query examples. Different from traditional QBME methods which accept one input example at a time, all the input examples are processed at the same time in this strategy. The experimental results demonstrate the effectiveness of the proposed method on both retrieval performance and speed. (C) 2012 Elsevier B.V. All rights reserved.