Accumulated reconstruction error vector (AREV): a semantic representation for cross-media retrieval
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- 作者：Kai Liu (1) (3)
Shikui Wei (1) (3)
Yao Zhao (1) (3)
Zhenfeng Zhu (1) (3)
Yunchao Wei (1) (3)
Changsheng Xu (2)
1. Institute of Information Science ; Beijing Jiaotong University ; Beijing ; 100044 ; China
3. Beijing Key Laboratory of Advanced Information Science and Network Technology ; Beijing ; 100044 ; China
2. Institute of Automation ; Chinese Academy of Sciences ; Beijing ; 100190 ; China - 关键词：Cross ; media ; Accumulated reconstruction error vector ; Retrieval ; Consistency ; Dictionary learning
- 刊名：Multimedia Tools and Applications
- 出版年：2015
- 出版时间：January 2015
- 年：2015
- 卷：74
- 期：2
- 页码：561-576
- 全文大小：958 KB
- 参考文献：1. Baidu Image Search, http://stu.baidu.com/
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- 刊物主题：Multimedia Information Systems
Computer Communication Networks
Data Structures, Cryptology and Information Theory
Special Purpose and Application-Based Systems - 出版者：Springer Netherlands
- ISSN：1573-7721
文摘
Cross-media retrieval aims to automatically perform the content-based search procedure among various media types (e.g., image, video and text), in which media representation plays an important role for providing the heterogeneous similarity measure. In this work, a novel semantic representation of cross-media, called accumulated reconstruction error vector (AREV), is proposed, which includes category-specific dictionary learning, media sample reconstruction, and accumulative reconstruction error concatenation. Instead of directly learning the correlation relationship among heterogeneous items in the same semantic groups, the AREV projects individually their original feature descriptions into a shared semantic space, in which each component is semantic consistent for various media types due to the consistency in category information. Experiments on the commonly used datasets, i.e. Wikipedia dataset and NUS-Wide dataset, show the good performance in terms of effectiveness and efficiency.