Focusing on the problems in video semantic detection that the insufficience of labeled samples would seriously affect the performance of the detection and the performances of the base classifiers in ensemble learning would be improved deficiently due to noise in the pseudo-label samples, a semi-supervised ensemble learning algorithm based on pseudo-label confidence selection was proposed. Firstly, three base classifiers were trained in three different feature spaces to get the label vectors of the base classifiers. Secondly, the error between the maximum and submaximal probability of a certain class of weighted fusion samples and the error between the maximum probability of a certain class of samples and the average probability of the other classes of samples were introduced as the label confidences of the base classifiers, and the pseudo-label and integrated confidence of samples were obtained through fusing label vectors and label confidences. Thirdly, samples with high degree of integrated confidence were added to the labeled sample set, and base classifiers were trained iteratively. Finally, the trained base classifiers were integrated to detect the video semantic concept collaboratively. The average accuracy of the algorithm on the experimental data set UCF11 reaches 83.48%. Compared with Co-KNN-SVM algorithm, the average accuracy is increased by 3.48 percentage points. The selected pseudo-label by the algorithm can reflect the overall variation among the class of samples and other classes, as well as the uniqueness of the class of samples, which can reduce the risk of using pseudo-label samples, and effectively improve the accuracy of video semantic concept detection.

To reduce quantization error in vector quantization of Bag of Words (BoW) for video semantic detection and extract feature automatically and effectively, a new video semantic detection method based on Topographic Independent Component Analysis (TICA) and Gaussian Mixture Model (GMM) was proposed. Firstly, features of each video clip were extracted by TICA algorithm to learn complex invariant features from video clips. Secondly, the feature distribution of each video clip was described by GMM. Finally, a GMM supervector was created from GMM parameters and the GMM supervector for each shot was used as the input of an Support Vector Machine (SVM) for video semantic detection. A GMM can be regard as an extension of the BoW to a probabilistic framework, and thus, has less quantization error, better retaining the information in the original feature vectors. The experiments were conducted on the TRECVID 2012 and OV datasets. The experimental results show that compared with BoW and SIFT (Scale Invariant Feature Transform)-GMM algorithm, the proposed method can improve the mean average precision on both of the TRECVID 2012 and OV datasets for video semantic detection.