Cross-media retrieval is an interesting research topic,which seeks to remove the barriers among different modalities.To enable cross-media retrieval,it is needed to find the correlation measures between heterogeneous ...Cross-media retrieval is an interesting research topic,which seeks to remove the barriers among different modalities.To enable cross-media retrieval,it is needed to find the correlation measures between heterogeneous low-level features and to judge the semantic similarity.This paper presents a novel approach to learn cross-media correlation between visual features and auditory features for image-audio retrieval.A semi-supervised correlation preserving mapping(SSCPM)method is described to construct the isomorphic SSCPM subspace where canonical correlations between the original visual and auditory features are further preserved.Subspace optimization algorithm is proposed to improve the local image cluster and audio cluster quality in an interactive way.A unique relevance feedback strategy is developed to update the knowledge of cross-media correlation by learning from user behaviors,so retrieval performance is enhanced in a progressive manner.Experimental results show that the performance of our approach is effective.展开更多
This paper presents a cross-media semantic mining model （CSMM） based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are tr...This paper presents a cross-media semantic mining model （CSMM） based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are trained and constructed with STTS （Semantic Template Training System）, which are taken as the bridge to realize the transition from various low-level media feature to object semantic. Furthermore, we put forward a kind of double layers metadata structure to efficaciously store and manage mined low-level feature and high-level semantic. This model has broad application in lots of domains such as intelligent retrieval engine, medical diagnoses, multimedia design and so on.展开更多
This paper is to study the conditions of teachers’occupational stress and anxiety by using cross-media teaching method,and reveals the influence relationship between them.To this end,a questionnaire survey of 228 teac...This paper is to study the conditions of teachers’occupational stress and anxiety by using cross-media teaching method,and reveals the influence relationship between them.To this end,a questionnaire survey of 228 teachers using cross-media teaching method from 3 schools in Guangdong Province(China)was conducted.The conclu-sions are as follows:Teachers who use cross-media teaching method have high levels of occupational stress and anxiety,lack of leadership and administrative support,overloaded work,state anxiety and trait anxiety are all at a high level.Under general characteristics differences,gender does not constitute a factor causing occupational stress and anxiety of the teachers using cross-media teaching.With the increase in the use of cross-media teach-ing,teachers feel gradually increase of occupational stress and trait anxiety in more work tasks,and occupational stress and state anxiety shows ups and downs due to lack of school policy support.From the relationship between occupational stress and anxiety,occupational stress and sub-variables without leadership and administrative sup-port,overloaded work,relationships with colleagues,and relationships with parents are all positively correlates with anxiety and have significant positive effects.Thereinto,whether the influence of occupational stress sub-vari-able on anxiety,or the state anxiety and trait anxiety of the anxiety sub-variables,overloaded work and lack of leadership and administrative support have always been the key factors that cause anxiety.Therefore,if the school or the relevant organization provides appropriate support and assistance to cross-media teaching,or appropri-ately reduce heavy tasks of teachers in cross-media teaching,so as to relieve occupational press and anxiety of the teachers,create good teaching quality,and promote the development of teaching technology.展开更多
Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the stat...Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows： （1） theory and model for cross-media uniform representation; （2） cross-media correlation understanding and deep mining; （3） cross-media knowledge graph construction and learning methodologies; （4） cross-media knowledge evolution and reasoning; （5） cross-media description and generation; （6） cross-media intelligent engines; and （7） cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.展开更多
Recently,we designed a new experimental system MSearch,which is a cross-media meta-search system built on the database of the WikipediaMM task of ImageCLEF 2008.For a meta-search engine,the kernel problem is how to me...Recently,we designed a new experimental system MSearch,which is a cross-media meta-search system built on the database of the WikipediaMM task of ImageCLEF 2008.For a meta-search engine,the kernel problem is how to merge the results from multiple member search engines and provide a more effective rank list.This paper deals with a novel fusion model employing supervised learning.Our fusion model employs ranking SVM in training the fusion weight for each member search engine. We assume the fusion weight of each member search engine as a feature of a result document returned by the meta-search engine. For a returned result document,we first build a feature vector to represent the document,and set the value of each feature as the document's score returned by the corresponding member search engine.Then we construct a training set from the documents returned from the meta-search engine to learn the fusion parameter.Finally,we use the linear fusion model based on the overlap set to merge the results set.Experimental results show that our approach significantly improves the performance of the cross-media meta-search(MSearch) and outperforms many of the existing fusion methods.展开更多
With the rapid development of Internet and multimedia technology, cross-media retrieval is concerned to retrieve all the related media objects with multi-modality by submitting a query media object. Unfortunately, the...With the rapid development of Internet and multimedia technology, cross-media retrieval is concerned to retrieve all the related media objects with multi-modality by submitting a query media object. Unfortunately, the complexity and the heterogeneity of multi-modality have posed the following two major challenges for cross-media retrieval： 1） how to construct, a unified and compact model for media objects with multi-modality, 2） how to improve the performance of retrieval for large scale cross-media database. In this paper, we propose a novel method which is dedicate to solving these issues to achieve effective and accurate cross-media retrieval. Firstly, a multi-modality semantic relationship graph （MSRG） is constructed using the semantic correlation amongst the media objects with multi-modality. Secondly, all the media objects in MSRG are mapped onto an isomorphic semantic space. Further, an efficient indexing MK-tree based on heterogeneous data distribution is proposed to manage the media objects within the semantic space and improve the performance of cross-media retrieval. Extensive experiments on real large scale cross-media datasets indicate that our proposal dramatically improves the accuracy and efficiency of cross-media retrieval, outperforming the existing methods significantly.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 60533090 and 60773051)the Natural Science Foundation of Zhejiang Province (No. Y105395),China
文摘Cross-media retrieval is an interesting research topic,which seeks to remove the barriers among different modalities.To enable cross-media retrieval,it is needed to find the correlation measures between heterogeneous low-level features and to judge the semantic similarity.This paper presents a novel approach to learn cross-media correlation between visual features and auditory features for image-audio retrieval.A semi-supervised correlation preserving mapping(SSCPM)method is described to construct the isomorphic SSCPM subspace where canonical correlations between the original visual and auditory features are further preserved.Subspace optimization algorithm is proposed to improve the local image cluster and audio cluster quality in an interactive way.A unique relevance feedback strategy is developed to update the knowledge of cross-media correlation by learning from user behaviors,so retrieval performance is enhanced in a progressive manner.Experimental results show that the performance of our approach is effective.
基金Supported by the National Basic Research Program of China 973 Program (2007CB310801)the Specialized Research Fund for the Doctoral Program of Higer Education of China (20070486064)+1 种基金the Natural Science Foundation of Hubei Province (2007ABA038)the Programme of Introducing Talents of Discipline to Universities (B07037)
文摘This paper presents a cross-media semantic mining model （CSMM） based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are trained and constructed with STTS （Semantic Template Training System）, which are taken as the bridge to realize the transition from various low-level media feature to object semantic. Furthermore, we put forward a kind of double layers metadata structure to efficaciously store and manage mined low-level feature and high-level semantic. This model has broad application in lots of domains such as intelligent retrieval engine, medical diagnoses, multimedia design and so on.
基金supported by research funds from Sehan University in Korea,2022funded by the 2021 Provincial and Municipal Joint Fund Project of the Natural Science Foundation of Hunan Province(Fund Code:2021JJ50149).
文摘This paper is to study the conditions of teachers’occupational stress and anxiety by using cross-media teaching method,and reveals the influence relationship between them.To this end,a questionnaire survey of 228 teachers using cross-media teaching method from 3 schools in Guangdong Province(China)was conducted.The conclu-sions are as follows:Teachers who use cross-media teaching method have high levels of occupational stress and anxiety,lack of leadership and administrative support,overloaded work,state anxiety and trait anxiety are all at a high level.Under general characteristics differences,gender does not constitute a factor causing occupational stress and anxiety of the teachers using cross-media teaching.With the increase in the use of cross-media teach-ing,teachers feel gradually increase of occupational stress and trait anxiety in more work tasks,and occupational stress and state anxiety shows ups and downs due to lack of school policy support.From the relationship between occupational stress and anxiety,occupational stress and sub-variables without leadership and administrative sup-port,overloaded work,relationships with colleagues,and relationships with parents are all positively correlates with anxiety and have significant positive effects.Thereinto,whether the influence of occupational stress sub-vari-able on anxiety,or the state anxiety and trait anxiety of the anxiety sub-variables,overloaded work and lack of leadership and administrative support have always been the key factors that cause anxiety.Therefore,if the school or the relevant organization provides appropriate support and assistance to cross-media teaching,or appropri-ately reduce heavy tasks of teachers in cross-media teaching,so as to relieve occupational press and anxiety of the teachers,create good teaching quality,and promote the development of teaching technology.
基金supported by the National Natural Science Foundation of China(Nos.61371128,U1611461,61425025,and 61532005)
文摘Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows： （1） theory and model for cross-media uniform representation; （2） cross-media correlation understanding and deep mining; （3） cross-media knowledge graph construction and learning methodologies; （4） cross-media knowledge evolution and reasoning; （5） cross-media description and generation; （6） cross-media intelligent engines; and （7） cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.
基金Project supported by the National Natural Science Foundation of China(No.60605020)the National High-Tech R&D Program (863) of China(Nos.2006AA01Z320 and 2006AA010105)
文摘Recently,we designed a new experimental system MSearch,which is a cross-media meta-search system built on the database of the WikipediaMM task of ImageCLEF 2008.For a meta-search engine,the kernel problem is how to merge the results from multiple member search engines and provide a more effective rank list.This paper deals with a novel fusion model employing supervised learning.Our fusion model employs ranking SVM in training the fusion weight for each member search engine. We assume the fusion weight of each member search engine as a feature of a result document returned by the meta-search engine. For a returned result document,we first build a feature vector to represent the document,and set the value of each feature as the document's score returned by the corresponding member search engine.Then we construct a training set from the documents returned from the meta-search engine to learn the fusion parameter.Finally,we use the linear fusion model based on the overlap set to merge the results set.Experimental results show that our approach significantly improves the performance of the cross-media meta-search(MSearch) and outperforms many of the existing fusion methods.
基金supported by the National Natural Science Foundation of China under Grant Nos.61025007,60933001,61100024the National Basic Research 973 Program of China under Grant No.2011CB302200-G+1 种基金the National High Technology Research and Development 863 Program of China under Grant No.2012AA011004the Fundamental Research Funds for the Central Universities of China under Grant No.N110404011
文摘With the rapid development of Internet and multimedia technology, cross-media retrieval is concerned to retrieve all the related media objects with multi-modality by submitting a query media object. Unfortunately, the complexity and the heterogeneity of multi-modality have posed the following two major challenges for cross-media retrieval： 1） how to construct, a unified and compact model for media objects with multi-modality, 2） how to improve the performance of retrieval for large scale cross-media database. In this paper, we propose a novel method which is dedicate to solving these issues to achieve effective and accurate cross-media retrieval. Firstly, a multi-modality semantic relationship graph （MSRG） is constructed using the semantic correlation amongst the media objects with multi-modality. Secondly, all the media objects in MSRG are mapped onto an isomorphic semantic space. Further, an efficient indexing MK-tree based on heterogeneous data distribution is proposed to manage the media objects within the semantic space and improve the performance of cross-media retrieval. Extensive experiments on real large scale cross-media datasets indicate that our proposal dramatically improves the accuracy and efficiency of cross-media retrieval, outperforming the existing methods significantly.
