For news video images, caption recognizing is a useful and important step for content understanding. Caption locating is usually the first step of caption recognizing and this paper proposes a simple but effective cap...For news video images, caption recognizing is a useful and important step for content understanding. Caption locating is usually the first step of caption recognizing and this paper proposes a simple but effective caption locating algorithm called maximum feature score region （MFSR） based method, which mainly consists of two stages： In the first stage, up/down boundaries are attained by turning to edge map projection. Then, maximum feature score region is defined and left/right boundaries are achieved by utilizing MFSR. Experiments show that the proposed MFSR based method has superior and robust performance on news video images of different types.展开更多
Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in...Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in movies based on a string of IE-Role Nets. An IE-Role Net(interaction and emotion rolenet) models the emotion and interactions of roles in a shot of the movie. The whole movie is represented as a string of IE-Role Nets. Summarization of a movie is transformed into finding an optimal substring with user-specified summarization ratio.Hierarchical substring mining is conducted to find an optimal substring of the whole movie. We have conducted objective and subjective experiments on our method. Experimental results show the ability of our method to capture the semantic content of movies.展开更多
Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there...Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.展开更多
All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and va...All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and value information of objects could play fundamental roles in the process of information understanding and decisionmaking in human thinking.Therefore,the questions of where and how the content information and the value information be produced from the formal information become critical in the theory of information understanding and decision-making.A conjectural theory that may reasonably answer the question is presented here in the paper.展开更多
基金supported by National Natural Science Foundation of China(Nos.61272394,61201395 and61472119)the program for Science&Technology Innovation Talents in Universities of Henan Province(No.13HASTIT039)+1 种基金Henan Polytechnic University Innovative Research Team(No.T2014-3)Henan Polytechnic University Fund for Distinguished Young Scholars(No.J2013-2)
文摘For news video images, caption recognizing is a useful and important step for content understanding. Caption locating is usually the first step of caption recognizing and this paper proposes a simple but effective caption locating algorithm called maximum feature score region （MFSR） based method, which mainly consists of two stages： In the first stage, up/down boundaries are attained by turning to edge map projection. Then, maximum feature score region is defined and left/right boundaries are achieved by utilizing MFSR. Experiments show that the proposed MFSR based method has superior and robust performance on news video images of different types.
基金supported by the National Basic Research Program of China under Grant No.2011CB302200-Gthe National Natural Science Foundation of China under Grant Nos. 61370074 and 61402091the Fundamental Research Funds for the Central Universities of China under Grant Nos. N120404007 and N140404012
文摘Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in movies based on a string of IE-Role Nets. An IE-Role Net(interaction and emotion rolenet) models the emotion and interactions of roles in a shot of the movie. The whole movie is represented as a string of IE-Role Nets. Summarization of a movie is transformed into finding an optimal substring with user-specified summarization ratio.Hierarchical substring mining is conducted to find an optimal substring of the whole movie. We have conducted objective and subjective experiments on our method. Experimental results show the ability of our method to capture the semantic content of movies.
基金support of the Science&Technology Development Project of Hangzhou Province,China(Grant No.20162013A08)the Research Project Support for Education of Zhejiang Province,China(Grant No.Y201941372)。
文摘Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.
基金The work was supported in part by the National Natural Science Foundation of China(Grant Nos.60575034 and 60873001)。
文摘All kinds of sensing organs in humans are able to reflect only the formal factors of objects,named formal information.It is believed,however,that not only the formal information but also the content information and value information of objects could play fundamental roles in the process of information understanding and decisionmaking in human thinking.Therefore,the questions of where and how the content information and the value information be produced from the formal information become critical in the theory of information understanding and decision-making.A conjectural theory that may reasonably answer the question is presented here in the paper.
