A Novel \(\ell ^1\)-graph Based Image Classification Algorithm
In original sparse representation based classification algorithms, each training sample belongs to exactly one class, neglecting the association between the training sample and the other classes. However, different classes’ features are visually similar and correlated (e.g. facial images), which means the association between the training sample and the different classes contain important information, and must be taken into consideration. In this paper, we propose a novel \(\ell ^1\)-graph based image classification algorithm (LGC). Our algorithm can automatically calculate associations between training samples and all classes, which are used for future classification. We evaluate our method on some popular visual benchmarks, the experimental results prove the effectiveness of our method.
KeywordsImage classification Sparse representation \(\ell ^1\)-graph
This research is supported in part by the Major State Basic Research Development Program of China (973 Program, 2012CB315803), the National Natural Science Foundation of China (61371078), and the Research Fund for the Doctoral Program of Higher Education of China (20130002110051).
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