Cubic-splines neural network- based system for Image Retrieval
Research in content-based image retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and...
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creator | Sadek, S. Al-Hamadi, A. Michaelis, B. Sayed, U. |
description | Research in content-based image retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named SNNIR (splines neural network-based image retrieval). SNNIR system makes use of a rapid and precise neural model. This model employs a cubic-splines activation function. By using the spline neural model, the gap between the low-level visual features and the high-level concepts is minimized. Experimental results show that the proposed system achieves high accuracy and effectiveness in terms of precision and recall compared with other CBIR systems. |
doi_str_mv | 10.1109/ICIP.2009.5413561 |
format | Conference Proceeding |
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So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named SNNIR (splines neural network-based image retrieval). SNNIR system makes use of a rapid and precise neural model. This model employs a cubic-splines activation function. By using the spline neural model, the gap between the low-level visual features and the high-level concepts is minimized. 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So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named SNNIR (splines neural network-based image retrieval). SNNIR system makes use of a rapid and precise neural model. This model employs a cubic-splines activation function. By using the spline neural model, the gap between the low-level visual features and the high-level concepts is minimized. Experimental results show that the proposed system achieves high accuracy and effectiveness in terms of precision and recall compared with other CBIR systems.</description><subject>Artificial neural networks</subject><subject>Content based retrieval</subject><subject>Cubic-splines neural network</subject><subject>Feature extraction</subject><subject>Image retrieval</subject><subject>Indexing</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Pathology</subject><subject>Polynomials</subject><subject>Process design</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>9781424456536</isbn><isbn>1424456533</isbn><isbn>9781424456550</isbn><isbn>9781424456543</isbn><isbn>142445655X</isbn><isbn>1424456541</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkM1KxDAURuMfWMd5AHHTF8iYm-SmyUaQ4k9hQBFdD2lzK9V2Zkg6yry9BWfj6iwOfHA-xq5ALACEu6nK6mUhhXAL1KDQwBGbu8KCllqjQRTHLJPKAreo3ck_p8wpywCl5Npacc4uUvoUQgpQkLHbcld3DU_bvltTyte0i76fMP5s4hfPa58o5GmfRhrydhPzavAflL_SGDv69v0lO2t9n2h-4Iy9P9y_lU98-fxYlXdL3kipRl774IikKmrXNmQhWKdNa81ksYbgBCosZADbNqikh3Yq0SZ4MAZljUHN2PXfbkdEq23sBh_3q8MV6hc7tUzt</recordid><startdate>200911</startdate><enddate>200911</enddate><creator>Sadek, S.</creator><creator>Al-Hamadi, A.</creator><creator>Michaelis, B.</creator><creator>Sayed, U.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200911</creationdate><title>Cubic-splines neural network- based system for Image Retrieval</title><author>Sadek, S. ; Al-Hamadi, A. ; Michaelis, B. ; Sayed, U.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c223t-bad9ee237b9fce81d8946f86c225b1d9053572d18fc532a1f38146da16652b5d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Artificial neural networks</topic><topic>Content based retrieval</topic><topic>Cubic-splines neural network</topic><topic>Feature extraction</topic><topic>Image retrieval</topic><topic>Indexing</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Pathology</topic><topic>Polynomials</topic><topic>Process design</topic><toplevel>online_resources</toplevel><creatorcontrib>Sadek, S.</creatorcontrib><creatorcontrib>Al-Hamadi, A.</creatorcontrib><creatorcontrib>Michaelis, B.</creatorcontrib><creatorcontrib>Sayed, U.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sadek, S.</au><au>Al-Hamadi, A.</au><au>Michaelis, B.</au><au>Sayed, U.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Cubic-splines neural network- based system for Image Retrieval</atitle><btitle>2009 16th IEEE International Conference on Image Processing (ICIP)</btitle><stitle>ICIP</stitle><date>2009-11</date><risdate>2009</risdate><spage>273</spage><epage>276</epage><pages>273-276</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>9781424456536</isbn><isbn>1424456533</isbn><eisbn>9781424456550</eisbn><eisbn>9781424456543</eisbn><eisbn>142445655X</eisbn><eisbn>1424456541</eisbn><abstract>Research in content-based image retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named SNNIR (splines neural network-based image retrieval). SNNIR system makes use of a rapid and precise neural model. This model employs a cubic-splines activation function. By using the spline neural model, the gap between the low-level visual features and the high-level concepts is minimized. Experimental results show that the proposed system achieves high accuracy and effectiveness in terms of precision and recall compared with other CBIR systems.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2009.5413561</doi><tpages>4</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Artificial neural networks Content based retrieval Cubic-splines neural network Feature extraction Image retrieval Indexing Neural networks Neurons Pathology Polynomials Process design |
title | Cubic-splines neural network- based system for Image Retrieval |
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