Geometrically modeled derivative feature descriptor aiding supervised shape retrieval
Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
S, Priyanka [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 48(2018), 12 vom: 09. Aug., Seite 4960-4975 |
|---|---|
| Übergeordnetes Werk: |
volume:48 ; year:2018 ; number:12 ; day:09 ; month:08 ; pages:4960-4975 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10489-018-1251-x |
|---|
| Katalog-ID: |
OLC2066106127 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2066106127 | ||
| 003 | DE-627 | ||
| 005 | 20230502205011.0 | ||
| 007 | tu | ||
| 008 | 200820s2018 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10489-018-1251-x |2 doi | |
| 035 | |a (DE-627)OLC2066106127 | ||
| 035 | |a (DE-He213)s10489-018-1251-x-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a S, Priyanka |e verfasserin |0 (orcid)0000-0003-4243-9006 |4 aut | |
| 245 | 1 | 0 | |a Geometrically modeled derivative feature descriptor aiding supervised shape retrieval |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC, part of Springer Nature 2018 | ||
| 520 | |a Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. | ||
| 650 | 4 | |a Classification | |
| 650 | 4 | |a Law of sines | |
| 650 | 4 | |a Shape descriptor | |
| 650 | 4 | |a Tetrakis square tiling | |
| 650 | 4 | |a Triangulated second-order shape derivative | |
| 700 | 1 | |a M S, Sudhakar |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Applied intelligence |d Springer US, 1991 |g 48(2018), 12 vom: 09. Aug., Seite 4960-4975 |w (DE-627)130990515 |w (DE-600)1080229-0 |w (DE-576)029154286 |x 0924-669X |7 nnns |
| 773 | 1 | 8 | |g volume:48 |g year:2018 |g number:12 |g day:09 |g month:08 |g pages:4960-4975 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10489-018-1251-x |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 48 |j 2018 |e 12 |b 09 |c 08 |h 4960-4975 | ||
| author_variant |
p s ps s s m ss ssm |
|---|---|
| matchkey_str |
article:0924669X:2018----::emtialmdldeiaieetrdsrpoadnsp |
| hierarchy_sort_str |
2018 |
| publishDate |
2018 |
| allfields |
10.1007/s10489-018-1251-x doi (DE-627)OLC2066106127 (DE-He213)s10489-018-1251-x-p DE-627 ger DE-627 rakwb eng 004 VZ S, Priyanka verfasserin (orcid)0000-0003-4243-9006 aut Geometrically modeled derivative feature descriptor aiding supervised shape retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. Classification Law of sines Shape descriptor Tetrakis square tiling Triangulated second-order shape derivative M S, Sudhakar aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 12 vom: 09. Aug., Seite 4960-4975 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:12 day:09 month:08 pages:4960-4975 https://doi.org/10.1007/s10489-018-1251-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 12 09 08 4960-4975 |
| spelling |
10.1007/s10489-018-1251-x doi (DE-627)OLC2066106127 (DE-He213)s10489-018-1251-x-p DE-627 ger DE-627 rakwb eng 004 VZ S, Priyanka verfasserin (orcid)0000-0003-4243-9006 aut Geometrically modeled derivative feature descriptor aiding supervised shape retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. Classification Law of sines Shape descriptor Tetrakis square tiling Triangulated second-order shape derivative M S, Sudhakar aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 12 vom: 09. Aug., Seite 4960-4975 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:12 day:09 month:08 pages:4960-4975 https://doi.org/10.1007/s10489-018-1251-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 12 09 08 4960-4975 |
| allfields_unstemmed |
10.1007/s10489-018-1251-x doi (DE-627)OLC2066106127 (DE-He213)s10489-018-1251-x-p DE-627 ger DE-627 rakwb eng 004 VZ S, Priyanka verfasserin (orcid)0000-0003-4243-9006 aut Geometrically modeled derivative feature descriptor aiding supervised shape retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. Classification Law of sines Shape descriptor Tetrakis square tiling Triangulated second-order shape derivative M S, Sudhakar aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 12 vom: 09. Aug., Seite 4960-4975 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:12 day:09 month:08 pages:4960-4975 https://doi.org/10.1007/s10489-018-1251-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 12 09 08 4960-4975 |
| allfieldsGer |
10.1007/s10489-018-1251-x doi (DE-627)OLC2066106127 (DE-He213)s10489-018-1251-x-p DE-627 ger DE-627 rakwb eng 004 VZ S, Priyanka verfasserin (orcid)0000-0003-4243-9006 aut Geometrically modeled derivative feature descriptor aiding supervised shape retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. Classification Law of sines Shape descriptor Tetrakis square tiling Triangulated second-order shape derivative M S, Sudhakar aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 12 vom: 09. Aug., Seite 4960-4975 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:12 day:09 month:08 pages:4960-4975 https://doi.org/10.1007/s10489-018-1251-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 12 09 08 4960-4975 |
| allfieldsSound |
10.1007/s10489-018-1251-x doi (DE-627)OLC2066106127 (DE-He213)s10489-018-1251-x-p DE-627 ger DE-627 rakwb eng 004 VZ S, Priyanka verfasserin (orcid)0000-0003-4243-9006 aut Geometrically modeled derivative feature descriptor aiding supervised shape retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. Classification Law of sines Shape descriptor Tetrakis square tiling Triangulated second-order shape derivative M S, Sudhakar aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 12 vom: 09. Aug., Seite 4960-4975 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:12 day:09 month:08 pages:4960-4975 https://doi.org/10.1007/s10489-018-1251-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 12 09 08 4960-4975 |
| language |
English |
| source |
Enthalten in Applied intelligence 48(2018), 12 vom: 09. Aug., Seite 4960-4975 volume:48 year:2018 number:12 day:09 month:08 pages:4960-4975 |
| sourceStr |
Enthalten in Applied intelligence 48(2018), 12 vom: 09. Aug., Seite 4960-4975 volume:48 year:2018 number:12 day:09 month:08 pages:4960-4975 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Classification Law of sines Shape descriptor Tetrakis square tiling Triangulated second-order shape derivative |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Applied intelligence |
| authorswithroles_txt_mv |
S, Priyanka @@aut@@ M S, Sudhakar @@aut@@ |
| publishDateDaySort_date |
2018-08-09T00:00:00Z |
| hierarchy_top_id |
130990515 |
| dewey-sort |
14 |
| id |
OLC2066106127 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2066106127</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205011.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-018-1251-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066106127</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-018-1251-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">S, Priyanka</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-4243-9006</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Geometrically modeled derivative feature descriptor aiding supervised shape retrieval</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Law of sines</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Shape descriptor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tetrakis square tiling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Triangulated second-order shape derivative</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">M S, Sudhakar</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">48(2018), 12 vom: 09. Aug., Seite 4960-4975</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:48</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:12</subfield><subfield code="g">day:09</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:4960-4975</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-018-1251-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">48</subfield><subfield code="j">2018</subfield><subfield code="e">12</subfield><subfield code="b">09</subfield><subfield code="c">08</subfield><subfield code="h">4960-4975</subfield></datafield></record></collection>
|
| author |
S, Priyanka |
| spellingShingle |
S, Priyanka ddc 004 misc Classification misc Law of sines misc Shape descriptor misc Tetrakis square tiling misc Triangulated second-order shape derivative Geometrically modeled derivative feature descriptor aiding supervised shape retrieval |
| authorStr |
S, Priyanka |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130990515 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0924-669X |
| topic_title |
004 VZ Geometrically modeled derivative feature descriptor aiding supervised shape retrieval Classification Law of sines Shape descriptor Tetrakis square tiling Triangulated second-order shape derivative |
| topic |
ddc 004 misc Classification misc Law of sines misc Shape descriptor misc Tetrakis square tiling misc Triangulated second-order shape derivative |
| topic_unstemmed |
ddc 004 misc Classification misc Law of sines misc Shape descriptor misc Tetrakis square tiling misc Triangulated second-order shape derivative |
| topic_browse |
ddc 004 misc Classification misc Law of sines misc Shape descriptor misc Tetrakis square tiling misc Triangulated second-order shape derivative |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Applied intelligence |
| hierarchy_parent_id |
130990515 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Applied intelligence |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 |
| title |
Geometrically modeled derivative feature descriptor aiding supervised shape retrieval |
| ctrlnum |
(DE-627)OLC2066106127 (DE-He213)s10489-018-1251-x-p |
| title_full |
Geometrically modeled derivative feature descriptor aiding supervised shape retrieval |
| author_sort |
S, Priyanka |
| journal |
Applied intelligence |
| journalStr |
Applied intelligence |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
txt |
| container_start_page |
4960 |
| author_browse |
S, Priyanka M S, Sudhakar |
| container_volume |
48 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
S, Priyanka |
| doi_str_mv |
10.1007/s10489-018-1251-x |
| normlink |
(ORCID)0000-0003-4243-9006 |
| normlink_prefix_str_mv |
(orcid)0000-0003-4243-9006 |
| dewey-full |
004 |
| title_sort |
geometrically modeled derivative feature descriptor aiding supervised shape retrieval |
| title_auth |
Geometrically modeled derivative feature descriptor aiding supervised shape retrieval |
| abstract |
Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 |
| container_issue |
12 |
| title_short |
Geometrically modeled derivative feature descriptor aiding supervised shape retrieval |
| url |
https://doi.org/10.1007/s10489-018-1251-x |
| remote_bool |
false |
| author2 |
M S, Sudhakar |
| author2Str |
M S, Sudhakar |
| ppnlink |
130990515 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10489-018-1251-x |
| up_date |
2024-07-04T03:46:46.858Z |
| _version_ |
1803618674925371392 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2066106127</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205011.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-018-1251-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066106127</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-018-1251-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">S, Priyanka</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-4243-9006</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Geometrically modeled derivative feature descriptor aiding supervised shape retrieval</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recent research on shape retrieval render highly acute feature descriptors that are computationally intensive. Hence, a simple approach through a novel tessellated version of the Tetrakis Square tiling scheme for acute feature descriptor aiding supervised shape retrieval is contributed in this paper. The proposed descriptor labeled as Triangulated Second-Order Shape Derivative (TSOSD) performs feature characterization and abstraction by fusing hybrid geometrical concepts with image derivative operators. First, the mechanism tessellates the image into square tiles that are later organized as right-angled triangles. Secondly, the derivatives from the right-angled triangular neighbors interact locally using the trigonometric identities to produce an angle-based feature map. Finally, the feature descriptor is then formulated by local segmentation of the attained feature maps to produce the shape histogram. Experimental results on three standard benchmark databases demonstrate the effectiveness of the proposed approach, particularly rendering a consistent retrieval rate greater than 95% in comparison with the state-of-the-art methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Law of sines</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Shape descriptor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tetrakis square tiling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Triangulated second-order shape derivative</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">M S, Sudhakar</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">48(2018), 12 vom: 09. Aug., Seite 4960-4975</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:48</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:12</subfield><subfield code="g">day:09</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:4960-4975</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-018-1251-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">48</subfield><subfield code="j">2018</subfield><subfield code="e">12</subfield><subfield code="b">09</subfield><subfield code="c">08</subfield><subfield code="h">4960-4975</subfield></datafield></record></collection>
|
| score |
7.400141 |