A new Local Radon Descriptor for Content-Based Image Search
Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this paper, we introduce a new simple descriptor based...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Babaie, Morteza Kashani, Hany Kumar, Meghana D Tizhoosh, Hamid. R |
| description | Content-based image retrieval (CBIR) is an essential part of computer vision
research, especially in medical expert systems. Having a discriminative image
descriptor with the least number of parameters for tuning is desirable in CBIR
systems. In this paper, we introduce a new simple descriptor based on the
histogram of local Radon projections. We also propose a very fast
convolution-based local Radon estimator to overcome the slow process of Radon
projections. We performed our experiments using pathology images (KimiaPath24)
and lung CT patches and test our proposed solution for medical image
processing. We achieved superior results compared with other histogram-based
descriptors such as LBP and HoG as well as some pre-trained CNNs. |
| doi_str_mv | 10.48550/arxiv.2007.15523 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2007_15523</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2007_15523</sourcerecordid><originalsourceid>FETCH-LOGICAL-a673-ec95e80a2c6c36e521b2006b2cbcfcfe5d81b9c389cf7e25cc9a0d57d16b355b3</originalsourceid><addsrcrecordid>eNotj8tOwzAQRb1hgQofwKr-gQQ_Ok4iViVAqRSpUuk-Go_HEKlNKifi8fcthcXV2V2dI8SdVvmiBFD3mL67z9woVeQawNhr8bCUPX_JZiDcyy2GoZdPPFLqjtOQZDyvHvqJ-yl7xJGDXB_wneUbY6KPG3EVcT_y7T9nYvfyvKtfs2azWtfLJkNX2IypAi4VGnJkHYPR_izgvCFPkSJDKLWvyJYVxYINEFWoAhRBO28BvJ2J-d_txb49pu6A6af9rWgvFfYE7NRBjw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A new Local Radon Descriptor for Content-Based Image Search</title><source>arXiv.org</source><creator>Babaie, Morteza ; Kashani, Hany ; Kumar, Meghana D ; Tizhoosh, Hamid. R</creator><creatorcontrib>Babaie, Morteza ; Kashani, Hany ; Kumar, Meghana D ; Tizhoosh, Hamid. R</creatorcontrib><description>Content-based image retrieval (CBIR) is an essential part of computer vision
research, especially in medical expert systems. Having a discriminative image
descriptor with the least number of parameters for tuning is desirable in CBIR
systems. In this paper, we introduce a new simple descriptor based on the
histogram of local Radon projections. We also propose a very fast
convolution-based local Radon estimator to overcome the slow process of Radon
projections. We performed our experiments using pathology images (KimiaPath24)
and lung CT patches and test our proposed solution for medical image
processing. We achieved superior results compared with other histogram-based
descriptors such as LBP and HoG as well as some pre-trained CNNs.</description><identifier>DOI: 10.48550/arxiv.2007.15523</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2020-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2007.15523$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2007.15523$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Babaie, Morteza</creatorcontrib><creatorcontrib>Kashani, Hany</creatorcontrib><creatorcontrib>Kumar, Meghana D</creatorcontrib><creatorcontrib>Tizhoosh, Hamid. R</creatorcontrib><title>A new Local Radon Descriptor for Content-Based Image Search</title><description>Content-based image retrieval (CBIR) is an essential part of computer vision
research, especially in medical expert systems. Having a discriminative image
descriptor with the least number of parameters for tuning is desirable in CBIR
systems. In this paper, we introduce a new simple descriptor based on the
histogram of local Radon projections. We also propose a very fast
convolution-based local Radon estimator to overcome the slow process of Radon
projections. We performed our experiments using pathology images (KimiaPath24)
and lung CT patches and test our proposed solution for medical image
processing. We achieved superior results compared with other histogram-based
descriptors such as LBP and HoG as well as some pre-trained CNNs.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tOwzAQRb1hgQofwKr-gQQ_Ok4iViVAqRSpUuk-Go_HEKlNKifi8fcthcXV2V2dI8SdVvmiBFD3mL67z9woVeQawNhr8bCUPX_JZiDcyy2GoZdPPFLqjtOQZDyvHvqJ-yl7xJGDXB_wneUbY6KPG3EVcT_y7T9nYvfyvKtfs2azWtfLJkNX2IypAi4VGnJkHYPR_izgvCFPkSJDKLWvyJYVxYINEFWoAhRBO28BvJ2J-d_txb49pu6A6af9rWgvFfYE7NRBjw</recordid><startdate>20200730</startdate><enddate>20200730</enddate><creator>Babaie, Morteza</creator><creator>Kashani, Hany</creator><creator>Kumar, Meghana D</creator><creator>Tizhoosh, Hamid. R</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20200730</creationdate><title>A new Local Radon Descriptor for Content-Based Image Search</title><author>Babaie, Morteza ; Kashani, Hany ; Kumar, Meghana D ; Tizhoosh, Hamid. R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a673-ec95e80a2c6c36e521b2006b2cbcfcfe5d81b9c389cf7e25cc9a0d57d16b355b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Babaie, Morteza</creatorcontrib><creatorcontrib>Kashani, Hany</creatorcontrib><creatorcontrib>Kumar, Meghana D</creatorcontrib><creatorcontrib>Tizhoosh, Hamid. R</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Babaie, Morteza</au><au>Kashani, Hany</au><au>Kumar, Meghana D</au><au>Tizhoosh, Hamid. R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new Local Radon Descriptor for Content-Based Image Search</atitle><date>2020-07-30</date><risdate>2020</risdate><abstract>Content-based image retrieval (CBIR) is an essential part of computer vision
research, especially in medical expert systems. Having a discriminative image
descriptor with the least number of parameters for tuning is desirable in CBIR
systems. In this paper, we introduce a new simple descriptor based on the
histogram of local Radon projections. We also propose a very fast
convolution-based local Radon estimator to overcome the slow process of Radon
projections. We performed our experiments using pathology images (KimiaPath24)
and lung CT patches and test our proposed solution for medical image
processing. We achieved superior results compared with other histogram-based
descriptors such as LBP and HoG as well as some pre-trained CNNs.</abstract><doi>10.48550/arxiv.2007.15523</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2007.15523 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2007_15523 |
| source | arXiv.org |
| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | A new Local Radon Descriptor for Content-Based Image Search |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-23T01%3A39%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20new%20Local%20Radon%20Descriptor%20for%20Content-Based%20Image%20Search&rft.au=Babaie,%20Morteza&rft.date=2020-07-30&rft_id=info:doi/10.48550/arxiv.2007.15523&rft_dat=%3Carxiv_GOX%3E2007_15523%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |