Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

SNMFCA: Supervised NMF-based image classification and annotation

Language

English

Abstract

In this paper, we propose a novel supervised nonnegative matrix factorization-based framework for both image classification and annotation. The framework consists of two phases: training and prediction. In the training phase, two supervised nonnegative matrix factorizations for image descriptors and annotation terms are combined to identify the latent image bases, and to represent the training images in the bases space. These latent bases can capture the representation of the images in terms of both descriptors and annotation terms. Based on the new representation of training images, classifiers can be learnt and built. In the prediction phase, a test image is first represented by the latent bases via solving a linear least squares problem, and then its class label and annotation can be predicted via the trained classifiers and the proposed annotation mapping model. In the algorithm, we develop a three-block proximal alternating nonnegative least squares algorithm to determine the latent image bases, and show its convergent property. Extensive experiments on real-world image data sets suggest that the proposed framework is able to predict the label and annotation for testing images successfully. Experimental results have also shown that our algorithm is computationally efficient and effective for image classification and annotation. © 2012 IEEE.

Keywords

Image annotation, image classification, latent image bases, nonnegative matrix factorization, supervised learning

Publication Date

2012

Source Publication Title

IEEE Transactions on Image Processing

Volume

21

Issue

11

Start Page

4508

End Page

4521

Publisher

Institute of Electrical and Electronics Engineers

DOI

10.1109/TIP.2012.2206040

Link to Publisher's Edition

http://dx.doi.org/10.1109/TIP.2012.2206040

ISSN (print)

10577149

ISSN (electronic)

19410042

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