As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image...As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.展开更多
Numerical models defined by means of a suitably assumed set of parameters make it possible to select the optimal structural solution for the given or assumed conditions. The paper presents examples of applications of ...Numerical models defined by means of a suitably assumed set of parameters make it possible to select the optimal structural solution for the given or assumed conditions. The paper presents examples of applications of numerical models defined in the programming language Formian during the shaping processes of various types of spatial structural systems designed for roof covers. These types of numerical models can be relatively easily adapted to the requirements, which can be frequently changed during the investment process, what makes possible a considerable reducing of costs and time of design of the space structures having even the very complex shapes. The advantageous features of application of numerical models defined in Formian are presented in models determined for selected forms of the roof covers designed also by means of a simple type of a space frame. In the paper, there are some presented visualizations made on bases of these models defining mainly for structural systems developed recently by the author for certain types of the dome covers. The proposed structural systems are built by means of the successive spatial hoops or they are created as unique forms of the geodesic dome structures.展开更多
基金Project supported by the Hi-Tech Research and Development Pro-gram (863) of China (No. 2003AA119010), and China-American Digital Academic Library (CADAL) Project (No. CADAL2004002)
文摘As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.
文摘Numerical models defined by means of a suitably assumed set of parameters make it possible to select the optimal structural solution for the given or assumed conditions. The paper presents examples of applications of numerical models defined in the programming language Formian during the shaping processes of various types of spatial structural systems designed for roof covers. These types of numerical models can be relatively easily adapted to the requirements, which can be frequently changed during the investment process, what makes possible a considerable reducing of costs and time of design of the space structures having even the very complex shapes. The advantageous features of application of numerical models defined in Formian are presented in models determined for selected forms of the roof covers designed also by means of a simple type of a space frame. In the paper, there are some presented visualizations made on bases of these models defining mainly for structural systems developed recently by the author for certain types of the dome covers. The proposed structural systems are built by means of the successive spatial hoops or they are created as unique forms of the geodesic dome structures.
