期刊论文、会议论文

Peng, Xiaojiang

英文

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

0302-9743

2014

8691 LNCS

PART 3

660-674

13th European Conference on Computer Vision (ECCV)

Lecture Notes in Computer Science

SEP 06-12, 2014

Zurich, SWITZERLAND

[Peng, Xiaojiang;Peng, Qiang] Southwest Jiaotong Univ, Chengdu, Peoples R China.^[Wang, Limin] Chinese Univ Hong Kong, Dept Informat Engn, Hong Kong, Peoples R China.^[Peng, Xiaojiang;Wang, Limin;Qiao, Yu] Chinese Acad Sci, Shenzhen Key Lab CVPR, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China.^[Wang, Limin] Hengyang Normal Univ, Hengyang, Peoples R China.

Fleet, D Pajdla, T Schiele, B Tuytelaars, T

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

978-3-319-10578-9; 978-3-319-10577-2

10.1007/978-3-319-10578-9_43

Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [91320101, 61036008, 60972111]; Shenzhen Basic Research Program [JC201005270350A, JCYJ20120903092050890, JCYJ201 20617114614438]; 100 Talents Program of CASChinese Academy of Sciences; Guangdong Innovative Research Team Program [201001D0104648280]; Hunan province

本校为其他机构

Recent studies show that aggregating local descriptors into super vector yields effective representation for retrieval and classification tasks. A popular method along this line is vector of locally aggregated descriptors (VLAD), which aggregates the residuals between descriptors and visual words. However, original VLAD ignores high-order statistics of local descriptors and its dictionary may not be optimal for classification tasks. In this paper, we address these problems by utilizing high-order statistics of local descriptors and peforming su...