A Novel Web Image Retrieval Method
Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introd...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Wenjin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 103(2018), 2 vom: 18. Jan., Seite 1153-1160 |
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| Übergeordnetes Werk: |
volume:103 ; year:2018 ; number:2 ; day:18 ; month:01 ; pages:1153-1160 |
| Links: |
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| DOI / URN: |
10.1007/s11277-018-5283-7 |
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OLC205382347X |
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| 520 | |a Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. | ||
| 650 | 4 | |a Image processing | |
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| 650 | 4 | |a Web search | |
| 650 | 4 | |a Web crawler | |
| 650 | 4 | |a Feature extraction | |
| 650 | 4 | |a Pseudo-Zernike moment | |
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10.1007/s11277-018-5283-7 doi (DE-627)OLC205382347X (DE-He213)s11277-018-5283-7-p DE-627 ger DE-627 rakwb eng 620 VZ Li, Wenjin verfasserin aut A Novel Web Image Retrieval Method 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. Image processing Image retrieval Web search Web crawler Feature extraction Pseudo-Zernike moment Enthalten in Wireless personal communications Springer US, 1994 103(2018), 2 vom: 18. Jan., Seite 1153-1160 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:103 year:2018 number:2 day:18 month:01 pages:1153-1160 https://doi.org/10.1007/s11277-018-5283-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 103 2018 2 18 01 1153-1160 |
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10.1007/s11277-018-5283-7 doi (DE-627)OLC205382347X (DE-He213)s11277-018-5283-7-p DE-627 ger DE-627 rakwb eng 620 VZ Li, Wenjin verfasserin aut A Novel Web Image Retrieval Method 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. Image processing Image retrieval Web search Web crawler Feature extraction Pseudo-Zernike moment Enthalten in Wireless personal communications Springer US, 1994 103(2018), 2 vom: 18. Jan., Seite 1153-1160 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:103 year:2018 number:2 day:18 month:01 pages:1153-1160 https://doi.org/10.1007/s11277-018-5283-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 103 2018 2 18 01 1153-1160 |
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10.1007/s11277-018-5283-7 doi (DE-627)OLC205382347X (DE-He213)s11277-018-5283-7-p DE-627 ger DE-627 rakwb eng 620 VZ Li, Wenjin verfasserin aut A Novel Web Image Retrieval Method 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. Image processing Image retrieval Web search Web crawler Feature extraction Pseudo-Zernike moment Enthalten in Wireless personal communications Springer US, 1994 103(2018), 2 vom: 18. Jan., Seite 1153-1160 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:103 year:2018 number:2 day:18 month:01 pages:1153-1160 https://doi.org/10.1007/s11277-018-5283-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 103 2018 2 18 01 1153-1160 |
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10.1007/s11277-018-5283-7 doi (DE-627)OLC205382347X (DE-He213)s11277-018-5283-7-p DE-627 ger DE-627 rakwb eng 620 VZ Li, Wenjin verfasserin aut A Novel Web Image Retrieval Method 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. Image processing Image retrieval Web search Web crawler Feature extraction Pseudo-Zernike moment Enthalten in Wireless personal communications Springer US, 1994 103(2018), 2 vom: 18. Jan., Seite 1153-1160 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:103 year:2018 number:2 day:18 month:01 pages:1153-1160 https://doi.org/10.1007/s11277-018-5283-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 103 2018 2 18 01 1153-1160 |
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Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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