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.

Focusing on the issue that the 3-point and 4-point hill climbing algorithms have high calculation and low efficiency in enhancing the nonlinearity of a Substitution box (S-box), an algorithm named Combination of Hill Climbing (CHC), which could apply multiple swap elements at a time, was proposed. The algorithm defined the behavior of swapping 2 output data of an S-box as a swap element, and used weighting prioritizing function to select swap elements that have larger contribution to the enhancement of nonlinearity, then simultaneously applied multiple selected swap elements to enhance the nonlinearity of an S-box. In the experiments, a maximum of 12 output data were swapped at a time by using the CHC algorithm, and most of the random 8-input and 8-output S-boxes' nonlinearity surpassed 102, with a maximum of 106. The experimental results show that the proposed CHC algorithm not only reduces the amount of calculation, but also enhances the nonlinearity of random S-boxes more significantly in comparison with the 3-point and 4-point hill climbing algorithms.