Multi technique amalgamation for enhanced information identification with content based image data

Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous im...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Das, Rik [verfasserIn] Thepade, Sudeep [verfasserIn] Ghosh, Saurav [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Image classification Image retrieval Otsu’s threshold Slant transform Morphological operator Fusion test

Übergeordnetes Werk:	Enthalten in: SpringerPlus - London : Biomed Central, 2012, 4(2015), 1 vom: 01. Dez.
Übergeordnetes Werk:	volume:4 ; year:2015 ; number:1 ; day:01 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s40064-015-1515-4

Katalog-ID:	SPR032769180

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allfields	10.1186/s40064-015-1515-4 doi (DE-627)SPR032769180 (SPR)s40064-015-1515-4-e DE-627 ger DE-627 rakwb eng 600 ASE Das, Rik verfasserin aut Multi technique amalgamation for enhanced information identification with content based image data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques. Image classification (dpeaa)DE-He213 Image retrieval (dpeaa)DE-He213 Otsu’s threshold (dpeaa)DE-He213 Slant transform (dpeaa)DE-He213 Morphological operator (dpeaa)DE-He213 Fusion (dpeaa)DE-He213 test (dpeaa)DE-He213 Thepade, Sudeep verfasserin aut Ghosh, Saurav verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 4(2015), 1 vom: 01. Dez. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:4 year:2015 number:1 day:01 month:12 https://dx.doi.org/10.1186/s40064-015-1515-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2015 1 01 12
spelling	10.1186/s40064-015-1515-4 doi (DE-627)SPR032769180 (SPR)s40064-015-1515-4-e DE-627 ger DE-627 rakwb eng 600 ASE Das, Rik verfasserin aut Multi technique amalgamation for enhanced information identification with content based image data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques. Image classification (dpeaa)DE-He213 Image retrieval (dpeaa)DE-He213 Otsu’s threshold (dpeaa)DE-He213 Slant transform (dpeaa)DE-He213 Morphological operator (dpeaa)DE-He213 Fusion (dpeaa)DE-He213 test (dpeaa)DE-He213 Thepade, Sudeep verfasserin aut Ghosh, Saurav verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 4(2015), 1 vom: 01. Dez. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:4 year:2015 number:1 day:01 month:12 https://dx.doi.org/10.1186/s40064-015-1515-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2015 1 01 12
allfields_unstemmed	10.1186/s40064-015-1515-4 doi (DE-627)SPR032769180 (SPR)s40064-015-1515-4-e DE-627 ger DE-627 rakwb eng 600 ASE Das, Rik verfasserin aut Multi technique amalgamation for enhanced information identification with content based image data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques. Image classification (dpeaa)DE-He213 Image retrieval (dpeaa)DE-He213 Otsu’s threshold (dpeaa)DE-He213 Slant transform (dpeaa)DE-He213 Morphological operator (dpeaa)DE-He213 Fusion (dpeaa)DE-He213 test (dpeaa)DE-He213 Thepade, Sudeep verfasserin aut Ghosh, Saurav verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 4(2015), 1 vom: 01. Dez. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:4 year:2015 number:1 day:01 month:12 https://dx.doi.org/10.1186/s40064-015-1515-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2015 1 01 12
allfieldsGer	10.1186/s40064-015-1515-4 doi (DE-627)SPR032769180 (SPR)s40064-015-1515-4-e DE-627 ger DE-627 rakwb eng 600 ASE Das, Rik verfasserin aut Multi technique amalgamation for enhanced information identification with content based image data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques. Image classification (dpeaa)DE-He213 Image retrieval (dpeaa)DE-He213 Otsu’s threshold (dpeaa)DE-He213 Slant transform (dpeaa)DE-He213 Morphological operator (dpeaa)DE-He213 Fusion (dpeaa)DE-He213 test (dpeaa)DE-He213 Thepade, Sudeep verfasserin aut Ghosh, Saurav verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 4(2015), 1 vom: 01. Dez. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:4 year:2015 number:1 day:01 month:12 https://dx.doi.org/10.1186/s40064-015-1515-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2015 1 01 12
allfieldsSound	10.1186/s40064-015-1515-4 doi (DE-627)SPR032769180 (SPR)s40064-015-1515-4-e DE-627 ger DE-627 rakwb eng 600 ASE Das, Rik verfasserin aut Multi technique amalgamation for enhanced information identification with content based image data 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques. Image classification (dpeaa)DE-He213 Image retrieval (dpeaa)DE-He213 Otsu’s threshold (dpeaa)DE-He213 Slant transform (dpeaa)DE-He213 Morphological operator (dpeaa)DE-He213 Fusion (dpeaa)DE-He213 test (dpeaa)DE-He213 Thepade, Sudeep verfasserin aut Ghosh, Saurav verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 4(2015), 1 vom: 01. Dez. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:4 year:2015 number:1 day:01 month:12 https://dx.doi.org/10.1186/s40064-015-1515-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2015 1 01 12
language	English
source	Enthalten in SpringerPlus 4(2015), 1 vom: 01. Dez. volume:4 year:2015 number:1 day:01 month:12
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topic_facet	Image classification Image retrieval Otsu’s threshold Slant transform Morphological operator Fusion test
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authorswithroles_txt_mv	Das, Rik @@aut@@ Thepade, Sudeep @@aut@@ Ghosh, Saurav @@aut@@
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title	Multi technique amalgamation for enhanced information identification with content based image data
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title_full	Multi technique amalgamation for enhanced information identification with content based image data
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title_sort	multi technique amalgamation for enhanced information identification with content based image data
title_auth	Multi technique amalgamation for enhanced information identification with content based image data
abstract	Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques.
abstractGer	Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques.
abstract_unstemmed	Abstract Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques.
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title_short	Multi technique amalgamation for enhanced information identification with content based image data
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