标题:Supervised Class Graph Preserving Hashing for Image Retrieval and Classification
作者:Feng, Lu; Xu, Xin-Shun; Guo, Shanqing; Wang, Xiao-Lin
通讯作者:Xu, XinShun
作者机构:[Feng, Lu; Xu, Xin-Shun; Guo, Shanqing; Wang, Xiao-Lin] Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China.
会议名称:23rd International Conference on MultiMedia Modeling (MMM)
会议日期:JAN 04-06, 2017
来源:MULTIMEDIA MODELING (MMM 2017), PT I
出版年:2017
卷:10132
页码:391-403
DOI:10.1007/978-3-319-51811-4_32
关键词:Hashing; Image retrieval; Image classification; Similarity search
摘要:With the explosive growth of data, hashing-based techniques have attracted significant attention due to their efficient retrieval and storage reduction ability. However, most hashing methods do not have the ability of predicting the labels directly. In this paper, we propose a novel supervised hashing approach, namely Class Graph Preserving Hashing (CGPH), which can well incorporate label information into hashing codes and classify the samples with binary codes directly. Specifically, CGPH learns hashing functions by ensuring label consistency and preserving class graph similarity among hashing codes simultaneously. Then, it learns effective binary codes through orthogonal transformation by minimizing the quantization error between hashing function and binary codes. In addition, an iterative method is proposed for the optimization problem in CGPH. Extensive experiments on two large scale real-world image data sets show that CGPH outperforms or is comparable to state-of-the-art hashing methods in both image retrieval and classification tasks.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009785709&doi=10.1007%2f978-3-319-51811-4_32&partnerID=40&md5=22d9bf544328e9eb37b1e939746f03ed
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