Interactive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper, we propose a novel framework for interactive imag...Interactive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper, we propose a novel framework for interactive image segmentation that builds upon graph-based manifold ranking model, a graph-based semi-supervised learning technique which can learn very smooth functions with respect to the intrinsic structure revealed by the input data. The final segmentation results are improved by overcoming two core problems of graph construction in traditional models: graph structure and graph edge weights. The user provided scribbles are treated as the must-link and must-not-link constraints. Then we model the graph as an approximatively k-regular sparse graph by integrating these constraints and our extended neighboring spatial relationships into graph structure modeling. The content and labels driven locally adaptive kernel parameter is proposed to tackle the insufficiency of previous models which usually employ a unified kernel parameter. After the graph construction,a novel three-stage strategy is proposed to get the final segmentation results. Due to the sparsity and extended neighboring relationships of our constructed graph and usage of superpixels, our model can provide nearly real-time, user scribble insensitive segmentations which are two core demands in interactive image segmentation. Last but not least, our framework is very easy to be extended to multi-label segmentation,and for some less complicated scenarios, it can even get the segmented object through single line interaction. Experimental results and comparisons with other state-of-the-art methods demonstrate that our framework can efficiently and accurately extract foreground objects from background.展开更多
Ranking is a main research issue in IR-styled keyword search over a set of documents. In this paper, we study a new keyword search problem, called context-sensitive document ranking, which is to rank documents with an...Ranking is a main research issue in IR-styled keyword search over a set of documents. In this paper, we study a new keyword search problem, called context-sensitive document ranking, which is to rank documents with an additional context that provides additional information about the application domain where the documents are to be searched and ranked. The work is motivated by the fact that additional information associated with the documents can possibly assist users to find more relevant documents when they are unable to find the needed documents from the documents alone. In this paper, a context is a multi-attribute graph, which can represent any information maintained in a relational database, where multi-attribute nodes represent tuples, and edges represent primary key and foreign key references among nodes. The context-sensitive ranking is related to several research issues, how to score documents, how to evaluate the additional information obtained in the context that may contribute to the document ranking, how to rank the documents by combining the scores/costs from the documents and the context. More importantly, the relationships between documents and the information stored in a relational database may be uncertain, because they are from different data sources and the relationships are determined systematically using similarity match which causes uncertainty. In this paper, we concentrate ourselves on these research issues, and provide our solution on how to rank the documents in a context where there exist uncertainty between the documents and the context. We confirm the effectiveness of our approaches by conducting extensive experimental studies using real datasets. We present our findings in this paper.展开更多
Traditional DEA-based ranking techniques have some pitfalls such as ignoring the inherent differences among decision making units （DMUs）, or lacking a common weight-based ranking, etc. To overcome these obstacles, t...Traditional DEA-based ranking techniques have some pitfalls such as ignoring the inherent differences among decision making units （DMUs）, or lacking a common weight-based ranking, etc. To overcome these obstacles, the paper first examines the possible differences among all DMUs such as the technical efficiency difference, the preference structure difference and the within-group position difference. Based upon the above differences this paper induces an integrated ranking measurement which helps to give a fair and full ranking for all DMUs under evaluation. Following the three types of differences, this approach behaves greatly elaborately, accurately and reasonably. Finally, the results from the Olympics achievement evaluation approve the acceptability of this approach.展开更多
This paper used the modern evaluation method of DEA （Data Envelopment Analysis） to assess the comparative efficiency and then on the basis of this among multiple schemes chose the optimal scheme of agricultural prod...This paper used the modern evaluation method of DEA （Data Envelopment Analysis） to assess the comparative efficiency and then on the basis of this among multiple schemes chose the optimal scheme of agricultural production structure adjustment. Based on the results of DEA model, we dissected scale advantages of each discretionary scheme or plan. We examined scale advantages of each discretionary scheme, tested profoundly a definitive purpose behind not-DEA efficient, which elucidated the system and methodology to enhance these discretionary plans. At the end, another method had been proposed to rank and select the optimal scheme. The research was important to guide the practice if the modification of agricultural production industrial structure was carded on.展开更多
in the paper, the advanced assessment method of DEA （Date Envelopment Analysis） had been used to evaluate relative efficiency and select the optimal scheme of agricultural industrial structure adjustment. According ...in the paper, the advanced assessment method of DEA （Date Envelopment Analysis） had been used to evaluate relative efficiency and select the optimal scheme of agricultural industrial structure adjustment. According to the results of DEA models, we analyzed scale benefits of every optional schemes, probed deeply the ultimate reason for not DEA efficient, which clarified the method and approach to improve these optional schemes. Finally, a new method had been proposed to rank and select the optimal scheme. The research is significant to direct the practice of the adjustment of agricultural industrial structure.展开更多
基金supported by NSFC (National Natural Science Foundation of China, No. 61272326)the research grant of University of Macao (No. MYRG202(Y1L4)-FST11-WEH)the research grant of University of Macao (No. MYRG2014-00139-FST)
文摘Interactive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper, we propose a novel framework for interactive image segmentation that builds upon graph-based manifold ranking model, a graph-based semi-supervised learning technique which can learn very smooth functions with respect to the intrinsic structure revealed by the input data. The final segmentation results are improved by overcoming two core problems of graph construction in traditional models: graph structure and graph edge weights. The user provided scribbles are treated as the must-link and must-not-link constraints. Then we model the graph as an approximatively k-regular sparse graph by integrating these constraints and our extended neighboring spatial relationships into graph structure modeling. The content and labels driven locally adaptive kernel parameter is proposed to tackle the insufficiency of previous models which usually employ a unified kernel parameter. After the graph construction,a novel three-stage strategy is proposed to get the final segmentation results. Due to the sparsity and extended neighboring relationships of our constructed graph and usage of superpixels, our model can provide nearly real-time, user scribble insensitive segmentations which are two core demands in interactive image segmentation. Last but not least, our framework is very easy to be extended to multi-label segmentation,and for some less complicated scenarios, it can even get the segmented object through single line interaction. Experimental results and comparisons with other state-of-the-art methods demonstrate that our framework can efficiently and accurately extract foreground objects from background.
基金supported by the Research Grants Council of the Hong Kong SAR,China,under Grant Nos. 419008 and 419109
文摘Ranking is a main research issue in IR-styled keyword search over a set of documents. In this paper, we study a new keyword search problem, called context-sensitive document ranking, which is to rank documents with an additional context that provides additional information about the application domain where the documents are to be searched and ranked. The work is motivated by the fact that additional information associated with the documents can possibly assist users to find more relevant documents when they are unable to find the needed documents from the documents alone. In this paper, a context is a multi-attribute graph, which can represent any information maintained in a relational database, where multi-attribute nodes represent tuples, and edges represent primary key and foreign key references among nodes. The context-sensitive ranking is related to several research issues, how to score documents, how to evaluate the additional information obtained in the context that may contribute to the document ranking, how to rank the documents by combining the scores/costs from the documents and the context. More importantly, the relationships between documents and the information stored in a relational database may be uncertain, because they are from different data sources and the relationships are determined systematically using similarity match which causes uncertainty. In this paper, we concentrate ourselves on these research issues, and provide our solution on how to rank the documents in a context where there exist uncertainty between the documents and the context. We confirm the effectiveness of our approaches by conducting extensive experimental studies using real datasets. We present our findings in this paper.
基金supported partly by the National Natural Science Fundation of China for Innovative Research Groups(T0821001)the National Natural Science Fundation of China(70801056)University of Science and Technology of China Science Funds for Young Scholars.
文摘Traditional DEA-based ranking techniques have some pitfalls such as ignoring the inherent differences among decision making units （DMUs）, or lacking a common weight-based ranking, etc. To overcome these obstacles, the paper first examines the possible differences among all DMUs such as the technical efficiency difference, the preference structure difference and the within-group position difference. Based upon the above differences this paper induces an integrated ranking measurement which helps to give a fair and full ranking for all DMUs under evaluation. Following the three types of differences, this approach behaves greatly elaborately, accurately and reasonably. Finally, the results from the Olympics achievement evaluation approve the acceptability of this approach.
基金国家教育部科学技术重点研究项目(the Key Technologies Project of the Ministry of Education of China No.03144)海南省自然科学基金(the Natural Science Foundation of Hainan Province of China under Grant No.60533)。
文摘This paper used the modern evaluation method of DEA （Data Envelopment Analysis） to assess the comparative efficiency and then on the basis of this among multiple schemes chose the optimal scheme of agricultural production structure adjustment. Based on the results of DEA model, we dissected scale advantages of each discretionary scheme or plan. We examined scale advantages of each discretionary scheme, tested profoundly a definitive purpose behind not-DEA efficient, which elucidated the system and methodology to enhance these discretionary plans. At the end, another method had been proposed to rank and select the optimal scheme. The research was important to guide the practice if the modification of agricultural production industrial structure was carded on.
基金National Science and Technology Programming (Research and Paradigm of Persisting and High Efficiency Agriculture 2001-DA508-D09-4)
文摘in the paper, the advanced assessment method of DEA （Date Envelopment Analysis） had been used to evaluate relative efficiency and select the optimal scheme of agricultural industrial structure adjustment. According to the results of DEA models, we analyzed scale benefits of every optional schemes, probed deeply the ultimate reason for not DEA efficient, which clarified the method and approach to improve these optional schemes. Finally, a new method had been proposed to rank and select the optimal scheme. The research is significant to direct the practice of the adjustment of agricultural industrial structure.
