Image Annotation by kNN-Sparse Graph-Based Label Propagation over Noisily Tagged Web Images

被引:103
作者
Tang, Jinhui [1 ]
Hong, Richang [1 ]
Yan, Shuicheng [2 ]
Chua, Tat-Seng [1 ]
Qi, Guo-Jun [3 ]
Jain, Ramesh [4 ]
机构
[1] Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore
[2] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
[3] Univ Illinois, Dept Elect & Comp Engn, Urbana, IL 61801 USA
[4] Univ Calif Irvine, Bren Sch Informat & Comp Sci, Irvine, CA 92697 USA
关键词
Algorithms; Theory; Experimentation; Sparse graph; kNN; semi-supervised learning; label propagation; web image; noisy tags;
D O I
10.1145/1899412.1899418
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
In this article, we exploit the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images. To annotate the images more accurately, we propose a novel kNN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously. The sparse graph constructed by datum-wise one-vs-kNN sparse reconstructions of all samples can remove most of the semantically unrelated links among the data, and thus it is more robust and discriminative than the conventional graphs. Meanwhile, we apply the approximate k nearest neighbors to accelerate the sparse graph construction without loosing its effectiveness. More importantly, we propose an effective training label refinement strategy within this graph-based learning framework to handle the noise in the training labels, by bringing in a dual regularization for both the quantity and sparsity of the noise. We conduct extensive experiments on a real-world image database consisting of 55,615 Flickr images and noisily tagged training labels. The results demonstrate both the effectiveness and efficiency of the proposed approach and its capability to deal with the noise in the training labels.
引用
收藏
页数:15
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