Aiming at the ignorance of target structure in test topic distribution due to the lack of annotation in supervised Latent Dirichlet Allocation (sLDA) model, a sLDA fastener image classification model combined with global and local constraints (glc-LDA) was proposed. Firstly, the training images were manually labeled, and the training topic distribution with structural information was learned in sLDA model. Then, the test topic distribution was calculated to obtain the image category probabilities as global constraints, the topic similarities of training sub-blocks and test sub-blocks as local constraints. Finally, updated test topic distribution was obtained by weighted summation of training topic distribution with the product of global and local constraints as updated weights. The image category labels were obtained in Softmax classifier by the updated topics. The experimental results show that the proposed algorithm can express the structural information of fastener and compared with sLDA model, the distinction of each category of fastener images is enhanced, and the false detection rate is reduced by 55%.

According to the fact that the performance of skin-color detection is greatly affected by the illumination, a kind of skin-color detection algorithm with good stability was proposed. According to the characteristic of face symmetry, the pixel correction algorithm was used to replace too bright or too dark pixels on the face area with normal ones, and then an adaptive method was used for skin-color detection, in which the corresponding chroma threshold was determined dynamically by the brightness of pixel. The experimental results show that, compared to other algorithms such as the YCbCr single Gauss model for skin-color detection, more than 10% of positive detection rate was increased and the false positive rate was reduced by 5% with the proposed algorithm under different light intensity. Moreover, the stability of the proposed algorithm is significantly enhanced.