CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Locality Discriminative Coding for Image Classification
Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng; Xu CS(徐常胜)
2013-08
Conference NameACM International Conference on Internet Multimedia Computing and Service
Source PublicationICMICS
Conference Date2013-8
Conference Place安徽黄山
Abstract
The Bag-of-Words (BOW) based methods are widely used
in image classification. However, huge number of visual information
is omitted inevitably in the quantization step of
the BOW. Recently, NBNN and its improved methods like
Local NBNN were proposed to solve this problem. Nevertheless,
these methods do not perform better than the stateof-
the-art BOW based methods. In this paper, based on the
advantages of BOW and Local NBNN, we introduce a novel
locality discriminative coding (LDC) method. We convert
each low level local feature, such as SIFT, into code vector
using the Local Feature-to-Class distance other than by
k-means quantization. Extensive experimental results on 4
challenging benchmark datasets show that our LDC method
outperforms 6 state-of-the-art image classification methods
(3 based on NBNN, 3 based on BOW).
KeywordBag-of-words Feature Coding Discriminative
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11761
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorXu CS(徐常胜)
Affiliation中科院自动化研究所
Recommended Citation
GB/T 7714
Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,et al. Locality Discriminative Coding for Image Classification[C],2013.
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