caa a22 4500 605446660 CHVBK 20210128100128.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s11042-014-1968-4 doi (NATIONALLICENCE)springer-10.1007/s11042-014-1968-4 Accumulated reconstruction error vector (AREV): a semantic representation for cross-media retrieval [Elektronische Daten] [Kai Liu, Shikui Wei, Yao Zhao, Zhenfeng Zhu, Yunchao Wei, Changsheng Xu] 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. Springer Science+Business Media New York, 2014 Cross-media nationallicence Accumulated reconstruction error vector nationallicence Retrieval nationallicence Consistency nationallicence Dictionary learning nationallicence Liu Kai Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut Wei Shikui Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut Zhao Yao Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut Zhu Zhenfeng Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut Wei Yunchao Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut Xu Changsheng Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/2(2015-01-01), 561-576 1380-7501 74:2<561 2015 74 11042 https://doi.org/10.1007/s11042-014-1968-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-1968-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Kai Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Wei Shikui Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Zhao Yao Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Zhu Zhenfeng Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Wei Yunchao Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China aut NATIONALLICENCE 700 1- Xu Changsheng Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/2(2015-01-01), 561-576 1380-7501 74:2<561 2015 74 11042