Graph-based Dimensionality Reduction for KNN-based Image Annotation

被引:0
|
作者
Liu, Xi [1 ]
Liu, Rujie [1 ]
Li, Fei [1 ]
Cao, Qiong [1 ]
机构
[1] Fujitsu Res & Dev Ctr Co LTD, Beijing, Peoples R China
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
KNN-based image annotation method is proved to be very successful. However, it suffers from two issues: (1) high computational cost; (2) the difficulty of finding semantically similar images. In this paper, we propose a graph-based dimensionality reduction method to solve the two problems by adapting the locality sensitive discriminant analysis method [1] to multi-label setting. We first determine relevant and irrelevant images based on label information and construct relevant and irrelevant graphs by focusing on the visually similar relevant and irrelevant images. A linear feature transformation matrix is derived by considering the two graphs. The transformation can map the images to a low-dimensional subspace in which neighborhood relevant images are pulled closer while irrelevant images are pushed away. Thus the new feature after dimensionality reduction is quite fit for KNN-based image annotation. Experiments on the Corel dataset also demonstrate the effectiveness of our dimensionality reduction method for KNN-based image annotation.
引用
收藏
页码:1253 / 1256
页数:4
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