With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the sing...With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.展开更多
With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the sing...With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.展开更多
The orderly organelle interaction network is prerequisite for normal life activity of cell, ensuring a balance between communication and uniqueness of organelles. Disorder organelle interaction is implicated in the oc...The orderly organelle interaction network is prerequisite for normal life activity of cell, ensuring a balance between communication and uniqueness of organelles. Disorder organelle interaction is implicated in the occurrence and development of many diseases. An in-depth understanding of mechanisms of orderly organelle interaction helps to reveal the pathogenesis of related diseases. Chemical and genetic tools have identified the roles of specific proteins in orderly organelle interaction;however, little is known about the modes, functions and mechanisms of orderly interaction between organelles. With rapid development of imaging tools, deep-going insights into the structure feature of cell membranes have substantially improved our understanding of the mechanisms of ordered organelle interactions. This review summarizes the conventional molecular mechanism of orderly organelle interactions, and highlights the new progress regarding membrane structure and the novel structural mechanism of orderly organelle transport.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.6137022961370178+4 种基金61272067)National Key Technology R&D Program(Grant No.2013BAH72B01)MOE-China Mobile Research Fund(Grant No.MCM20130651)the Natural Science Foundation of GDP(Grant No.S2013010015178)Science-Technology Project of GDED(Grant No.2012KJCX0037)
文摘With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.
文摘With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.
基金This work was supported by the National Key R&D Program of China(No.2017YFA0505300)the National Natural Science Foundation of China(Nos.21727816,21721003)the Program of Laboratory for Marine Biology and Biotechnology,Pilot National Laboratory for Marine Science and Technology(Qingdao),China(No.MS2018NO08).
文摘The orderly organelle interaction network is prerequisite for normal life activity of cell, ensuring a balance between communication and uniqueness of organelles. Disorder organelle interaction is implicated in the occurrence and development of many diseases. An in-depth understanding of mechanisms of orderly organelle interaction helps to reveal the pathogenesis of related diseases. Chemical and genetic tools have identified the roles of specific proteins in orderly organelle interaction;however, little is known about the modes, functions and mechanisms of orderly interaction between organelles. With rapid development of imaging tools, deep-going insights into the structure feature of cell membranes have substantially improved our understanding of the mechanisms of ordered organelle interactions. This review summarizes the conventional molecular mechanism of orderly organelle interactions, and highlights the new progress regarding membrane structure and the novel structural mechanism of orderly organelle transport.
