Thinking of images as what they are: compound matrix regression for image classification

Authors:
Zhigang Ma;Yi Yang;Feiping Nie;Nicu Sebe
Affiliations:
University of Trento Italy;The University of Queensland, Australia;University of Texas at Arlington;University of Trento Italy
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

In this paper, we propose a new classification framework for image matrices. The approach is realized by learning two groups of classification vectors for each dimension of the image matrices. One novelty is that we utilize compound regression models in the learning process, which endows the algorithm increased degree of freedom. On top of that, we extend the two-dimensional classification method to a semi-supervised classifier which leverages both labeled and unlabeled data. A fast iterative solution is then proposed to solve the objective function. The proposed method is evaluated by several different applications. The experimental results show that our method outperforms several classification approaches. In addition, we observe that our method attains respectable classification performance even when only few labeled training samples are provided. This advantage is especially desirable for real-world problems since precisely annotated images are scarce.