Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Sparse label-indicator optimization methods for image classification

Language

English

Abstract

Image label prediction is a critical issue in computer vision and machine learning. In this paper, we propose and develop sparse label-indicator optimization methods for image classification problems. Sparsity is introduced in the label-indicator such that relevant and irrelevant images with respect to a given class can be distinguished. Also, when we deal with multi-class image classification problems, the number of possible classes of a given image can also be constrained to be small in which it is valid for natural images. The resulting sparsity model can be formulated as a convex optimization problem, and it can be solved very efficiently. Experimental results are reported to illustrate the effectiveness of the proposed model, and demonstrate that the classification performance of the proposed method is better than the other testing methods in this paper. © 2014 IEEE.

Keywords

Graph, image classification, multi-class, random walk with restart, semi-supervised learning, sparsity

Publication Date

2014

Source Publication Title

IEEE Transactions on Image Processing

Volume

23

Issue

3

Start Page

1002

End Page

1014

Publisher

Institute of Electrical and Electronics Engineers

ISSN (print)

10577149

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