NDLI: Large scale natural image classification by sparsity exploration

Content Provider	IEEE Xplore Digital Library
Author	Changhu Wang Shuicheng Yan Hong-Jiang Zhang
Copyright Year	2009
Description	Author affiliation: MOE-MS Key Lab of MCC, University of Science and Technology of China, China (Changhu Wang) \|\| Department of Electrical and Computer Engineering, National University of Singapore, Singapore (Shuicheng Yan) \|\| Advanced Technology Center, Microsoft Research, Beijing, China (Hong-Jiang Zhang)
Abstract	We consider in this paper the problem of large scale natural image classification. As the explosion and popularity of images in the Internet, there are increasing attentions to utilize millions of or even billions of these images for helping image related research. Beyond the opportunities brought by unlimited data, a great challenge is how to design more effective classification methods under these large scale scenarios. Most of existing attempts are based on k-nearest-neighbor method. However, in spite of the optimistic performance in some tasks, this strategy still suffers from that, one single fixed global parameter k is not robust for different object classes from different semantic levels. In this paper, we propose an alternative method, called $ℓ^{1}-nearest-neighbor,$ based on a sparse representation computed by $ℓ^{1}-minimization.$ We first treat a testing sample as a sparse linear combination of all training samples, and then consider the related samples as the nearest neighbors of the testing sample. Finally, we classify the testing sample based on the majority of these neighbors' classes. We conduct extensive experiments on a 1.6 million natural image database on different semantic levels defined based on WordNet, which demonstrate that the proposed $ℓ^{1}-nearest-neighbor$ algorithm outperforms k-nearest-neighbor in two aspects: 1) the robustness of parameter selection for different semantic levels, and 2) the discriminative capability for large scale image classification task.
Starting Page	3709
Ending Page	3712
File Size	122400
Page Count	4
File Format	PDF
ISBN	9781424423538
ISSN	15206149
DOI	10.1109/ICASSP.2009.4960432
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-19
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Large-scale systems Image classification Robustness Computer vision Signal processing Testing Signal processing algorithms Layout Image recognition Information retrieval WordNet sparsity ℓ¹-nearest-neighbor k-nearest-neighbor
Content Type	Text
Resource Type	Article

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