The traditional image labeling models often have two deficiencies; they only can model short-range contextual information in pixel-level of the image and have a complicated inference. To improve the precision of image labeling, the fully-connected Conditional Random Field (CRF) model was used; to simplify the inference of the model, the mean filed approximation based on Gaussian kd-tree for inference was proposed. To verify the effectiveness of the proposed algorithm, the experimental image datasets not only contained the standard picture library MSRC-9, but also contained MyDataset_1 (machine parts) and MyDataset_2 (office table) which made by authors. The precisions of the proposed method on those three datasets are 77.96%, 97.15% and 95.35% respectively, and the mean cost time of each picture is 2s. The results indicate that the fully-connected CRF model can improve the precision of image labeling by considering the contextual information of image and the mean field approximation using Gaussian kd-tree can raise the efficiency of inference.

To more effectively extract localized features of images, on the basis of the traditional Non-negative Sparse Coding (Hoyer NNSC) algorithm, a novel localized NNSC (LNNSC) algorithm with sparse constraint was proposed. This algorithm considered the sparse measure constraint of feature basis vectors and the maximized representativeness of features, and could obtain the strengthened localized image features. At the same time, this algorithm utilized the Laplace density model as the feature coefficients sparse punitive function to ensure an image's sparse structure. Furthermore, on the basis of feature extraction, by utilizing the Radial Basis Probabilistic Neural Networks (RBPNN), the palmprint recognition task could be implemented automatically. Compared with the palmprint recognition methods of Non-negative Matrix Factorization (NMF), Local NMF (LNMF) and Hoyer-NNSC, simulation results show that our method proposed here displays feasibility and practicality in palmprint recognition.