NDLI: A local learning based Image-To-Class distance for image classification

Content Provider	IEEE Xplore Digital Library
Author	Xinyuan Cai Baihua Xiao Chunheng Wang Rongguo Zhang
Copyright Year	2011
Description	Author affiliation: State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China (Xinyuan Cai; Baihua Xiao; Chunheng Wang; Rongguo Zhang)
Abstract	Image-To-Class distance is first proposed in Naive-Bayes Nearest-Neighbor. NBNN is a feature-based image classifier, and can achieve impressive classification accuracy. However, the performance of NBNN relies heavily on the large number of training samples. If using small number of training samples, the performance will degrade. The goal of this paper is to address this issue. The main contribution of this paper is that we propose a robust Image-to-Class distance by local learning. We define the patch-to-class distance as the distance between the input patch to its nearest neighbor in one class, which is reconstructed in the local manifold space; and then our image-to-class distance is the sum of patch-to-class distance. Furthermore, we take advantage of large-margin metric learning framework to obtain a proper Mahalanobis metric for each class. We evaluate the proposed method on four benchmark datasets: Caltech, Corel, Scene13, and Graz. The results show that our defined Image-To-Class Distance is more robust than NBNN and Optimal-NBNN, and by combining with the learned metric for each class, our method can achieve significant improvement over previous reported results on these datasets.
Starting Page	667
Ending Page	671
File Size	1165684
Page Count	5
File Format	PDF
ISBN	9781457701221
e-ISBN	9781457701214
DOI	10.1109/ACPR.2011.6166577
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Training Support vector machines Accuracy Local learning Naïve Bayes Nearest-Neighbor Robustness Large margin metric learning Image reconstruction Image-to-class distance Image classification
Content Type	Text
Resource Type	Article

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