Effective Heterogeneous Similarity Measure with Nearest Neighbors for Cross-Media Retrieval
Effective Heterogeneous Similarity Measure with Nearest Neighbors for Cross-Media Retrieval
复制标题
DOI：
10.1007/978-3-642-27355-1_30
复制
发表时间：
2012-01
期刊：
影响因子：
--
通讯作者：
Xiaohua Zhai;Yuxin Peng;Jianguo Xiao
中科院分区：
文献类型：
--
作者：
Xiaohua Zhai;Yuxin Peng;Jianguo Xiao
新兴的多媒体内容，包括图像和文本总是联合使用来描述相同的语义。因此，跨媒体检索变得越来越重要，它能够检索与查询语义相同但具有不同媒体类型的结果。本文提出了一种新的基于最近邻的异构相似性度量方法（HSNN）。与传统的相似性度量方法局限于同构特征空间不同，HSNN可以计算不同媒体类型的媒体对象之间的相似性。通过计算两个媒体对象属于同一语义类别的概率来获得异质相似性。通过分析每个媒体对象的同质最近邻来实现概率。HSNN是灵活的，所以任何传统的相似性度量可以被纳入，这进一步被视为弱排名。通过AdaRank从多个弱排序器中学习一个有效的排序模型，用于跨媒体检索。在wikipedia数据集上的实验表明，与现有方法相比，该方法是有效的。在单媒体检索任务上，跨媒体检索也表现出优于图像检索系统的性能。
Emerging multimedia content including images and texts are always jointly utilized to describe the same semantics. As a result, cross-media retrieval becomes increasingly important, which is able to retrieve the results of the same semantics with the query but with different media types. In this paper, we propose a novel heterogeneous similarity measure with nearest neighbors (HSNN). Unlike traditional similarity measures which are limited in homogeneous feature space, HSNN could compute the similarity between media objects with different media types. The heterogeneous similarity is obtained by computing the probability for two media objects belonging to the same semantic category. The probability is achieved by analyzing the homogeneous nearest neighbors of each media object. HSNN is flexible so that any traditional similarity measure could be incorporated, which is further regarded as the weak ranker. An effective ranking model is learned from multiple weak rankers through AdaRank for cross-media retrieval. Experiments on the wikipedia dataset show the effectiveness of the proposed approach, compared with state-of-the-art methods. The cross-media retrieval also shows to outperform image retrieval systems on a unimedia retrieval task.