A semantic model for general purpose content-based image retrieval systems
The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query im...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zarchi, Mohsen Sardari [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver - Couch, Yvonne ELSEVIER, 2014, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:40 ; year:2014 ; number:7 ; pages:2062-2071 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.compeleceng.2014.07.008 |
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| 520 | |a The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. | ||
| 520 | |a The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. | ||
| 700 | 1 | |a Monadjemi, Amirhasan |4 oth | |
| 700 | 1 | |a Jamshidi, Kamal |4 oth | |
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10.1016/j.compeleceng.2014.07.008 doi GBVA2014001000001.pica (DE-627)ELV02772221X (ELSEVIER)S0045-7906(14)00186-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zarchi, Mohsen Sardari verfasserin aut A semantic model for general purpose content-based image retrieval systems 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. Monadjemi, Amirhasan oth Jamshidi, Kamal oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:40 year:2014 number:7 pages:2062-2071 extent:10 https://doi.org/10.1016/j.compeleceng.2014.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 40 2014 7 2062-2071 10 045F 620 |
| spelling |
10.1016/j.compeleceng.2014.07.008 doi GBVA2014001000001.pica (DE-627)ELV02772221X (ELSEVIER)S0045-7906(14)00186-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zarchi, Mohsen Sardari verfasserin aut A semantic model for general purpose content-based image retrieval systems 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. Monadjemi, Amirhasan oth Jamshidi, Kamal oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:40 year:2014 number:7 pages:2062-2071 extent:10 https://doi.org/10.1016/j.compeleceng.2014.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 40 2014 7 2062-2071 10 045F 620 |
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10.1016/j.compeleceng.2014.07.008 doi GBVA2014001000001.pica (DE-627)ELV02772221X (ELSEVIER)S0045-7906(14)00186-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zarchi, Mohsen Sardari verfasserin aut A semantic model for general purpose content-based image retrieval systems 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. Monadjemi, Amirhasan oth Jamshidi, Kamal oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:40 year:2014 number:7 pages:2062-2071 extent:10 https://doi.org/10.1016/j.compeleceng.2014.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 40 2014 7 2062-2071 10 045F 620 |
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10.1016/j.compeleceng.2014.07.008 doi GBVA2014001000001.pica (DE-627)ELV02772221X (ELSEVIER)S0045-7906(14)00186-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zarchi, Mohsen Sardari verfasserin aut A semantic model for general purpose content-based image retrieval systems 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. Monadjemi, Amirhasan oth Jamshidi, Kamal oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:40 year:2014 number:7 pages:2062-2071 extent:10 https://doi.org/10.1016/j.compeleceng.2014.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 40 2014 7 2062-2071 10 045F 620 |
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10.1016/j.compeleceng.2014.07.008 doi GBVA2014001000001.pica (DE-627)ELV02772221X (ELSEVIER)S0045-7906(14)00186-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 530 VZ 43.13 bkl 50.17 bkl 58.53 bkl Zarchi, Mohsen Sardari verfasserin aut A semantic model for general purpose content-based image retrieval systems 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. Monadjemi, Amirhasan oth Jamshidi, Kamal oth Enthalten in Elsevier Science Couch, Yvonne ELSEVIER Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017356792 volume:40 year:2014 number:7 pages:2062-2071 extent:10 https://doi.org/10.1016/j.compeleceng.2014.07.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_23 GBV_ILN_40 GBV_ILN_70 43.13 Umwelttoxikologie VZ 50.17 Sicherheitstechnik VZ 58.53 Abfallwirtschaft VZ AR 40 2014 7 2062-2071 10 045F 620 |
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English |
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Enthalten in Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver Amsterdam [u.a.] volume:40 year:2014 number:7 pages:2062-2071 extent:10 |
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Enthalten in Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver Amsterdam [u.a.] volume:40 year:2014 number:7 pages:2062-2071 extent:10 |
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Brain-derived microvesicles confer sickness behaviours by switching on the acute phase response in the liver |
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Zarchi, Mohsen Sardari @@aut@@ Monadjemi, Amirhasan @@oth@@ Jamshidi, Kamal @@oth@@ |
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The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. |
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The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. |
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The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology. |
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A semantic model for general purpose content-based image retrieval systems |
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