An Image Mosaic Method Based on Convolutional Neural Network Semantic Features Extraction
Since traditional image feature extraction methods rely on features such as corner points, a new method based on semantic feature extraction is proposed inspiring by convolution neural attack. The semantic features of each pixel in an image are computed and quantified by neural network to represent...
Gespeichert in:
| Veröffentlicht in: | Journal of signal processing systems 2020-04, Vol.92 (4), p.435-444 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 444 |
|---|---|
| container_issue | 4 |
| container_start_page | 435 |
| container_title | Journal of signal processing systems |
| container_volume | 92 |
| creator | Shi, Zaifeng Li, Hui Cao, Qingjie Ren, Huizheng Fan, Boyu |
| description | Since traditional image feature extraction methods rely on features such as corner points, a new method based on semantic feature extraction is proposed inspiring by convolution neural attack. The semantic features of each pixel in an image are computed and quantified by neural network to represent the contribution of each pixel to the image semantics. According to the quantization results, the semantic contribution values of each pixel are sorted, and the semantic feature points are selected from high to low and the image mosaic is completed. Experimental results show that this method can effectively extract image features and complete image mosaic. |
| doi_str_mv | 10.1007/s11265-019-01477-2 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_webof</sourceid><recordid>TN_cdi_webofscience_primary_000519348800007CitationCount</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2376771931</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-1445acae9b03abb905ca975ba2b50049758ba3d1fd29bac309c306a04fb1c29b3</originalsourceid><addsrcrecordid>eNqNkMtOwzAQRS0EElD4AVaWWKLAOI86XkJEoRKFBbBgZY0dBwJtDLbD4-9xGx47xGI0o9G5o7mXkD0GhwyAH3nG0nGRABOxcs6TdI1sMZGJpGSsWP-egZWbZNv7R4Ax8IJtkbvjjk4XeG_ozHpsNZ2Z8GBreoLe1NR2tLLdq533obUdzuml6d2qhTfrnui1WWAXompiMPTOeHr6HhzqJb1DNhqce7P71UfkdnJ6U50nF1dn0-r4ItEZEyFheV6gRiMUZKiUgEKj4IXCVBUAeRxLhVnNmjoVCnUGItYYIW8U03GVjcj-cPfZ2Zfe-CAfbe_is16mGR9zHq2zSKUDpZ313plGPrt2ge5DMpDLCOUQoYwRylWEUT0i5SB6M8o2Xrem0-ZHCABFvJ2XZZyAV23Ape_K9l2I0oP_SyOdDbSPRHdv3K-HP977BFsjlNg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2376771931</pqid></control><display><type>article</type><title>An Image Mosaic Method Based on Convolutional Neural Network Semantic Features Extraction</title><source>SpringerNature Journals</source><source>Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><creator>Shi, Zaifeng ; Li, Hui ; Cao, Qingjie ; Ren, Huizheng ; Fan, Boyu</creator><creatorcontrib>Shi, Zaifeng ; Li, Hui ; Cao, Qingjie ; Ren, Huizheng ; Fan, Boyu</creatorcontrib><description>Since traditional image feature extraction methods rely on features such as corner points, a new method based on semantic feature extraction is proposed inspiring by convolution neural attack. The semantic features of each pixel in an image are computed and quantified by neural network to represent the contribution of each pixel to the image semantics. According to the quantization results, the semantic contribution values of each pixel are sorted, and the semantic feature points are selected from high to low and the image mosaic is completed. Experimental results show that this method can effectively extract image features and complete image mosaic.</description><identifier>ISSN: 1939-8018</identifier><identifier>EISSN: 1939-8115</identifier><identifier>DOI: 10.1007/s11265-019-01477-2</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial neural networks ; Circuits and Systems ; Computer Imaging ; Computer Science ; Computer Science, Information Systems ; Convolution ; Electrical Engineering ; Engineering ; Engineering, Electrical & Electronic ; Feature extraction ; Image Processing and Computer Vision ; Neural networks ; Pattern Recognition ; Pattern Recognition and Graphics ; Pixels ; Science & Technology ; Semantics ; Signal,Image and Speech Processing ; Technology ; Vision</subject><ispartof>Journal of signal processing systems, 2020-04, Vol.92 (4), p.435-444</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>2019© Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>17</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000519348800007</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c319t-1445acae9b03abb905ca975ba2b50049758ba3d1fd29bac309c306a04fb1c29b3</citedby><cites>FETCH-LOGICAL-c319t-1445acae9b03abb905ca975ba2b50049758ba3d1fd29bac309c306a04fb1c29b3</cites><orcidid>0000-0002-3851-5697</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11265-019-01477-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11265-019-01477-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,28253,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>Shi, Zaifeng</creatorcontrib><creatorcontrib>Li, Hui</creatorcontrib><creatorcontrib>Cao, Qingjie</creatorcontrib><creatorcontrib>Ren, Huizheng</creatorcontrib><creatorcontrib>Fan, Boyu</creatorcontrib><title>An Image Mosaic Method Based on Convolutional Neural Network Semantic Features Extraction</title><title>Journal of signal processing systems</title><addtitle>J Sign Process Syst</addtitle><addtitle>J SIGNAL PROCESS SYS</addtitle><description>Since traditional image feature extraction methods rely on features such as corner points, a new method based on semantic feature extraction is proposed inspiring by convolution neural attack. The semantic features of each pixel in an image are computed and quantified by neural network to represent the contribution of each pixel to the image semantics. According to the quantization results, the semantic contribution values of each pixel are sorted, and the semantic feature points are selected from high to low and the image mosaic is completed. Experimental results show that this method can effectively extract image features and complete image mosaic.</description><subject>Artificial neural networks</subject><subject>Circuits and Systems</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Convolution</subject><subject>Electrical Engineering</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Feature extraction</subject><subject>Image Processing and Computer Vision</subject><subject>Neural networks</subject><subject>Pattern Recognition</subject><subject>Pattern Recognition and Graphics</subject><subject>Pixels</subject><subject>Science & Technology</subject><subject>Semantics</subject><subject>Signal,Image and Speech Processing</subject><subject>Technology</subject><subject>Vision</subject><issn>1939-8018</issn><issn>1939-8115</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><recordid>eNqNkMtOwzAQRS0EElD4AVaWWKLAOI86XkJEoRKFBbBgZY0dBwJtDLbD4-9xGx47xGI0o9G5o7mXkD0GhwyAH3nG0nGRABOxcs6TdI1sMZGJpGSsWP-egZWbZNv7R4Ax8IJtkbvjjk4XeG_ozHpsNZ2Z8GBreoLe1NR2tLLdq533obUdzuml6d2qhTfrnui1WWAXompiMPTOeHr6HhzqJb1DNhqce7P71UfkdnJ6U50nF1dn0-r4ItEZEyFheV6gRiMUZKiUgEKj4IXCVBUAeRxLhVnNmjoVCnUGItYYIW8U03GVjcj-cPfZ2Zfe-CAfbe_is16mGR9zHq2zSKUDpZ313plGPrt2ge5DMpDLCOUQoYwRylWEUT0i5SB6M8o2Xrem0-ZHCABFvJ2XZZyAV23Ape_K9l2I0oP_SyOdDbSPRHdv3K-HP977BFsjlNg</recordid><startdate>20200401</startdate><enddate>20200401</enddate><creator>Shi, Zaifeng</creator><creator>Li, Hui</creator><creator>Cao, Qingjie</creator><creator>Ren, Huizheng</creator><creator>Fan, Boyu</creator><general>Springer US</general><general>Springer Nature</general><general>Springer Nature B.V</general><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-3851-5697</orcidid></search><sort><creationdate>20200401</creationdate><title>An Image Mosaic Method Based on Convolutional Neural Network Semantic Features Extraction</title><author>Shi, Zaifeng ; Li, Hui ; Cao, Qingjie ; Ren, Huizheng ; Fan, Boyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-1445acae9b03abb905ca975ba2b50049758ba3d1fd29bac309c306a04fb1c29b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Circuits and Systems</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer Science, Information Systems</topic><topic>Convolution</topic><topic>Electrical Engineering</topic><topic>Engineering</topic><topic>Engineering, Electrical & Electronic</topic><topic>Feature extraction</topic><topic>Image Processing and Computer Vision</topic><topic>Neural networks</topic><topic>Pattern Recognition</topic><topic>Pattern Recognition and Graphics</topic><topic>Pixels</topic><topic>Science & Technology</topic><topic>Semantics</topic><topic>Signal,Image and Speech Processing</topic><topic>Technology</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shi, Zaifeng</creatorcontrib><creatorcontrib>Li, Hui</creatorcontrib><creatorcontrib>Cao, Qingjie</creatorcontrib><creatorcontrib>Ren, Huizheng</creatorcontrib><creatorcontrib>Fan, Boyu</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>CrossRef</collection><jtitle>Journal of signal processing systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shi, Zaifeng</au><au>Li, Hui</au><au>Cao, Qingjie</au><au>Ren, Huizheng</au><au>Fan, Boyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Image Mosaic Method Based on Convolutional Neural Network Semantic Features Extraction</atitle><jtitle>Journal of signal processing systems</jtitle><stitle>J Sign Process Syst</stitle><stitle>J SIGNAL PROCESS SYS</stitle><date>2020-04-01</date><risdate>2020</risdate><volume>92</volume><issue>4</issue><spage>435</spage><epage>444</epage><pages>435-444</pages><issn>1939-8018</issn><eissn>1939-8115</eissn><abstract>Since traditional image feature extraction methods rely on features such as corner points, a new method based on semantic feature extraction is proposed inspiring by convolution neural attack. The semantic features of each pixel in an image are computed and quantified by neural network to represent the contribution of each pixel to the image semantics. According to the quantization results, the semantic contribution values of each pixel are sorted, and the semantic feature points are selected from high to low and the image mosaic is completed. Experimental results show that this method can effectively extract image features and complete image mosaic.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11265-019-01477-2</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-3851-5697</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1939-8018 |
| ispartof | Journal of signal processing systems, 2020-04, Vol.92 (4), p.435-444 |
| issn | 1939-8018 1939-8115 |
| language | eng |
| recordid | cdi_webofscience_primary_000519348800007CitationCount |
| source | SpringerNature Journals; Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /> |
| subjects | Artificial neural networks Circuits and Systems Computer Imaging Computer Science Computer Science, Information Systems Convolution Electrical Engineering Engineering Engineering, Electrical & Electronic Feature extraction Image Processing and Computer Vision Neural networks Pattern Recognition Pattern Recognition and Graphics Pixels Science & Technology Semantics Signal,Image and Speech Processing Technology Vision |
| title | An Image Mosaic Method Based on Convolutional Neural Network Semantic Features Extraction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-14T15%3A55%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_webof&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20Image%20Mosaic%20Method%20Based%20on%20Convolutional%20Neural%20Network%20Semantic%20Features%20Extraction&rft.jtitle=Journal%20of%20signal%20processing%20systems&rft.au=Shi,%20Zaifeng&rft.date=2020-04-01&rft.volume=92&rft.issue=4&rft.spage=435&rft.epage=444&rft.pages=435-444&rft.issn=1939-8018&rft.eissn=1939-8115&rft_id=info:doi/10.1007/s11265-019-01477-2&rft_dat=%3Cproquest_webof%3E2376771931%3C/proquest_webof%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2376771931&rft_id=info:pmid/&rfr_iscdi=true |