Aiming at the high-dimensional and small sample problems of functional Magnetic Resonance Imaging (fMRI) data, a Semi-Supervised Feature Selection by Spectral Clustering (SS-FSSC) model was proposed. Firstly, the prior brain region template was used to extract the time series signal. Then, the Pearson correlation coefficient and the Order Statistics Correlation Coefficient (OSCC) were selected to describe the functional connection features between the brain regions, and spectral clustering was performed to the features. Finally, the feature importance criterion based on Constraint score was adopted to select feature subsets, and the subsets were input into the Support Vector Machine (SVM) classifier for classification. By 100 times of five-fold cross-validation on the COBRE (Center for Biomedical Research Excellence) schizophrenia public dataset in the experiments, it is found that when the number of retained features is 152, the highest average accuracy of the proposed model to schizophrenia is about 77%, and the highest accuracy of the proposed model to schizophrenia is 95.83%. Experimental result analysis shows that by only retaining 16 functional connection features for classifier training, the model can stably achieve an average accuracy of more than 70%. In addition, in the results obtained by the proposed model, Intracalcarine Cortex has the highest occurrence frequency among the 10 brain regions corresponding to the functional connections, which is consistent to the existing research state about schizophrenia.

For the complex and autonomous landing scene is difficult to be recognized in wild flight environment for low-small-slow Unmanned Aerial Vehicles (UAV), a novel field scene recognition algorithm based on the combination of local pyramid feature and Convolutional Neural Network (CNN) learning feature was proposed. Firstly, the scene was divided into small scenes of 4×4 and 8×8 blocks. The Histogram of Oriented Gradient (HOG) algorithm was used to extract the scene features of all the blocks. All the features were connected end to end to get the feature vector with the characteristics of spatial pyramid. Secondly, a depth CNN aiming at the classification of scenes was designed. The method of tuning training was adopted to obtain CNN model and extract the characteristics of deep network learning. Finally, the two features were connected to get the final scene feature and the Support Vector Machine (SVM) classifier was used for classification. Compared with other traditional manual feature methods, the proposed algorithm can improve the recognition accuracy by more than 4 percentage points in data sets such as Sports-8, Scene-15, Indoor-67 and a self-built one. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the landing scene.

Concerning the problem that existing Intra Bock Copy (IBC) algorithm is not well suitable to the screen content with a variety of different sizes and shapes of patterns, an Intra Mini-Block Copy (IMBC) algorithm was proposed to further improve coding efficiency of the screen content. Firstly, the Coding Unit (CU) was divided into a set of L mini-blocks. Secondly, each mini-block was taken as the smallest matching and replication unit, the reference mini-block which matched the mini-block best was found in the reference set R by a mini-block matching optimal selection strategy. And the location of the reference mini-block and its location in the current CU were specified by L Displacement Vectors (DVs). Finally, a prediction algorithm was firstly applied to the L DVs for eliminating the correlation between DVs before entropy encoding. Compared with the IBC algorithm, for the High Efficiency Video Coding (HEVC), Screen Content Coding (SCC) test sequences, IMBC achieved the BD-rate reduction up to 3.4%, 2.9%, 2.6% for All Intra (AI), Random Access (RA) and Low-delay B (LB) configurations in lossy coding respectively, and the Bit-rate saving up to 9.5%, 5.2%, 5.1% for AI, RA, LB configurations in lossless coding respectively. The experimental results show that IMBC algorithm can effectively improve the coding efficiency of screen image at very low additional encoding and decoding complexity.

To solve the problem of that although Intra String Copy (ISC) improved the effect of the screen content coding, but it transformed the 2D image to 1D by Coding Unit (CU), making adjacent regions in an image segmented and spatial correlation not to be used, a new algorithm called 2D Intra String Copy (2D ISC) was proposed. Almost without additional memory in encoder and decoder, the algorithm realized arbitrary 2D shape searching and matching without boundary restriction of CU for pixels in current CU, by using dictionary coding tool in High Efficiency Video Coding (HEVC) reconstruction cache. Also adopted technologies of color quantization preprocessing and horizontal vertical search order self-adaption to enhance coding effect. Experiments on common test for typical screen content test sequences show that compared with HEVC, 2D ISC can achieve bit-rate saving of 46.5%, 34.8%, 25.4% for All Intra(AI), Random Access(RA) and Low-delay B(LB) configurations respectively in lossless coding mode, and 34.0%, 37.2%, 23.9% for AI, RA and LB configurations respectively in lossy coding mode. Even compared with ISC, 2D ISC can also achieve bit-rate saving up to 18.3%, 13.9%, 11.0% for AI, RA and LB configurations in lossless coding mode, and 19.8%, 20.5%, 10.4% for AI, RA and LB configurations in lossy coding mode. The experimental results indicate that the proposed algorithm is feasible and efficient.