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Sparse Label-Indicator Optimization Methods for Image Classification
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DOI：10.1109/TIP.2013.2294546.png)
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Abstract
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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.
Keywords:
Graph
multi-class
image classification
sparsity
random walk with restart
semi-supervised learning
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