Most existing visual sentiment analysis methods mainly construct visual sentiment feature representation based on the whole image. However, the local regions with objects in the image are able to highlight the sentiment better. Concerning the problem of ignorance of local regions sentiment representation in visual sentiment analysis, a visual sentiment analysis method by combining global and local regions of image was proposed. Image sentiment representation was mined by combining a whole image with local regions of the image. Firstly, an object detection model was used to locate the local regions with objects in the image. Secondly, the sentiment features of the local regions with objects were extracted by deep neural network. Finally, the deep features extracted from the whole image and the local region features were utilized to jointly train the image sentiment classifier and predict the sentiment polarity of the image. Experimental results show that the classification accuracy of the proposed method reaches 75.81% and 78.90% respectively on the real datasets TwitterⅠand TwitterⅡ, which is higher than the accuracy of sentiment analysis methods based on features extracted from the whole image or features extracted from the local regions of image.

In order to solve the problem of misjudgment which due to emotion point to an unknown and missing hidden view in traditional emotion classification method, a text sentiment classification method based on emotional role modeling was proposed. The method firstly identified evaluation objects in the text, and it used the measure based on local semantic analysis to tag the sentence emotion which had potential evaluation object. Then it distinguished the positive and negative polarity of evaluation objects in this paper by defining its emotional role. And it let the tendency value of emotional role integrate into feature space to improve the feature weight computation method. Finally, it proposed the concept named "features converge" to reduce the dimension of model. The experimental results show that the proposed method can improve the effect and accuracy of 3.2% for text sentiment classification effectively compared with other approaches which tend to pick the strong subjective emotional items as features.

A new image retrieval method based on enhanced micro-structure and context-sensitive similarity was proposed to overcome the shortcoming of high dimension of combined image feature and intangible combined weights. A new local pattern map was firstly used to create filter map, and then enhanced micro-structure descriptor was extracted based on color co-occurrence relationship. The descriptor combined several features with the same dimension as single color feature. Based on the extracted descriptor, normal distance between image pairs was calculated and sorted. Combined with the iterative context-sensitive similarity, the initial sorted image series were re-ranked. With setting the value of iteration times as 50 and considering the top 24 images in the retrieved image set, the comparative experiments with Multi-Texton Histogram (MTH) and Micro-Structure Descriptor (MSD) show that the retrieval precisions of the proposed algorithm respectively are increased by 13.14% and 7.09% on Corel-5000 image set and increased by 11.03% and 6.8% on Corel-10000 image set. By combining several features and using context information while keeping dimension unchanged, the new method can enhance the precision effectively.

The traditional algorithm of 3D face reconstruction is inefficient and it is difficult to meet the requirements of practical application. To address this problem, a feature-slice-based 3D face reconstruction algorithm was proposed. Besides, the feature-slice-based weighed 3D face recognition was proposed on the basis of the reconstruction algorithm. First, a 2D template-based alignment algorithm was developed to process the correspondence between faces automatically, and a linear facial model was built up. Second, an improved Active Shape Model (ASM) algorithm was proposed to locate the feature points and slices in the 3D and 2D face images. Then, every facial feature slices shape was reconstructed by a PCA-based sparse morphable mode. Finally, the algorithm was applied to 3D face recognition. The experimental results show that the presented algorithm has higher efficiency and accuracy, and improves the 3D face recognition rate.

In order to modify the incomplete road extraction caused by shadow, shelter and noise in high resolution remote sensing images, a Ribbon Snake model with width information was established based on the geometric characteristics of road. To overcome the great dependence of interior parameters and the easy being affected by the complex background of Ribbon Snake, a B-spline Ribbon Snake was constructed, where the smoothness of the Snake was implicit in the B-spline formulation and the flexibility of the Snake was adjusted by the number of control points. The road network segmentation results show that the improved B-spline Ribbon Snake can obtain a more accurate and smoother segmentation and is more robust to noise.