Yuan Jiazheng

A Novel Generalized SVM Algorithm with Application to Region-Based Image Retrieval

Support vector machines (SVM) has been widely applied in the area of content-based image retrieval in order to learn high-level concepts from low-level image features. Most existing SVM based image retrieval algorithms only rely on global-based features to represent the image content, which obviously can not well reflect the image semantic content. Region-based representations are far more close to the image content. However, such representations are of variable length and the Gaussian kernel is inappropriate in this situation. In this paper, a novel generalized SVM algorithm is proposed, which takes into account both low-level features and structural information of the image, in order to solve the problem of region-based image retrieval via SVM framework. Firstly, for a given image, salient regions are extracted and the concept of Salient Region Adjacency Graph is proposed to represent the image semantics. Secondly, based on the SRAG, a novel generalized structure kernel based SVM algorithm is constructed for content-based image retrieval. Experiments show that the proposed method shows better performance in image semantic retrieval than traditional method.

llumination Estimation Combining Physical and Statistical Approaches

llumination estimation for color constancy is an important problem in computer vision. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, the advantages of the two kinds are integrated. At first, a novel statistic-based algorithm called Illumination Estimation using K-nearest-neighbor (IE-KNN) is proposed. And then the physics-based Grey-Edge algorithm is used to extract image features for IE-KNN. One of the most important aims of this paper is to reduce the feature dimension in traditional statistics-based approaches. The experimental results show that this combined physical and statistical algorithm is effective and can achieved much better color constancy result.

Witkey websites based education of art and design

Witkey pattern can not only create a new employment model, but also affect the education model. Witkey websites have great influence on education and students because they can help more and more students with studying and finding jobs as new network platforms.

Blend-learning mode of artistic design education

Based on blend-learning and constructivism educational theory, comprehensive curriculum, assessment, and training resources can be designed to be perfect fit for college students who learn art and design so that students can be trained to use their brains more effectively.

A novel perception based image categorization algorithm

Image categorization plays an important role in the field of image retrieval and automatic annotation. Most existing algorithms adopted the low-level visual features to represent an image and the well-known Bag-of-words model followed by the SVM classifier was employed to fulfill the classification task. In this paper, instead of using the traditional low-level visual features, we employ the perception theory and propose a novel image categorization algorithm based on the representation of images topological properties, i.e., an image can be represented by a low-dimensional features (e.g., Euler characteristic, the number of holes) which is called the topological properties. Given the new representations of images, a naïve Bayes classifier is performed to fulfill the categorization task. The experimental results on the well-known image dataset show that based on the topological representation, the image categorization performance can be well improved.