Locality Discriminative Coding for Image Classification
Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng; Xu CS(徐常胜)
2013-08
会议名称ACM International Conference on Internet Multimedia Computing and Service
会议录名称ICMICS
会议日期2013-8
会议地点安徽黄山
摘要
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).
关键词Bag-of-words Feature Coding Discriminative
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11761
专题模式识别国家重点实验室_多媒体计算与图形学
通讯作者Xu CS(徐常胜)
作者单位中科院自动化研究所
推荐引用方式
GB/T 7714
Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,et al. Locality Discriminative Coding for Image Classification[C],2013.
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