The Feature Extraction and Recognition of Phone Image Based on Robust Sparse Non-Negative Matrix Factorization

ABSTRACT

Sparse non-negative matrix factorization algorithm can project image data effectively. It plays an important role in image matching and recognition. In order to improve the effectiveness of SNMF algorithm, which is used in feature extraction of image data with noises, we added a noise term and combined it with SNMF algorithm. Then, we proposed a new sparse optimization objective function and worked out its solution which can guarantee the sparseness of extracted feature and improve the algorithm’s immunity against noise at the same time. We name this robust sparse nonnegative matrix factorization (RSNMF) algorithm. It is applied for feature extraction and recognition of phone image. The concept of interface image and sub-graph of mobile phone is created. The feature extraction of phone image is used in RSNMF. And the features are put in support vector machine to achieve classification recognition. Experimental results shows that not only phone image data can be large-scale compressed through RSNMF algorithm with good robustness, but also the recognition efficiency is improved by generating sparse matrix as an intermediary target matrix to classification.

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