Locality-constrained self-taught learning for coal-rock recognition

In terms of the coal-rock image recognition problems in case of insufficient training samples,a new method of locality-constrained self-taught learning for coal-rock recognition was proposed. This method first obtains the diction- ary matrix from the unlabeled random images. These training samples are unlabeled non-coal-rock images from a com- mon data set,which are different from coal-rock images. Then the method uses the local linear constraint coding meth- od to get spare coding of coal-rock image samples,which are used as a feature of coal-rock image. Finally,the SVM classification algorithm is adopted to identify coal-rock images. Experiments show that the image high-level visual structures can be obtained from a large number of unlabeled random image samples through self-taught learning local constraint linear coding method,and the image features can be expressed concisely and effectively with these struc- tures. This algorithm has a wide range of applicability. Compared with the original coding algorithm, the proposed method is more efficient,and the average coal-rock image recognition accuracy increases by 1% -3% .