Cross-media retrieval using query dependent search methods

被引：33

作者：

Yang, Yi

Wu, Fei ^{[1
]}

Xu, Dong ^{[2
]}

Zhuang, Yueting

Chia, Liang-Tien ^{[2
]}

机构：

[1] Zhejiang Univ, Inst Artificial Intelligence, Coll Comp Sci, Hangzhou 310027, Peoples R China

[2] Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore

来源：

PATTERN RECOGNITION | 2010年 / 43卷 / 08期

基金：

中国国家自然科学基金; 国家高技术研究发展计划(863计划);

关键词：

Content-based multimedia retrieval; Relevance feedback; Multimedia document; Query expansion; DIMENSIONALITY; CLASSIFICATION;

D O I：

10.1016/j.patcog.2010.02.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The content-based cross-media retrieval is a new type of multimedia retrieval in which the media types of query examples and the returned results can be different. In order to learn the semantic correlations among multimedia objects of different modalities, the heterogeneous multimedia objects are analyzed in the form of multimedia document (MMD), which is a set of multimedia objects that are of different media types but carry the same semantics. We first construct an MMD semi-semantic graph (MMDSSG) by jointly analyzing the heterogeneous multimedia data. After that, cross-media indexing space (CMIS) is constructed. For each query, the optimal dimension of CMIS is automatically determined and the cross-media retrieval is performed on a per-query basis. By doing this, the most appropriate retrieval approach for each query is selected, i.e. different search methods are used for different queries. The query dependent search methods make cross-media retrieval performance not only accurate but also stable. We also propose different learning methods of relevance feedback (RF) to improve the performance. Experiment is encouraging and validates the proposed methods. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：2927 / 2936

页数：10

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