Editorial image retrieval using handcrafted and CNN features

Claudia Companioni-Brito; Mohamed Elawady; Sule Yildirim; Jon Yngve Hardeberg

doi:10.1007/978-3-319-94211-7_31

Editorial image retrieval using handcrafted and CNN features

Claudia Companioni-Brito^*, Mohamed Elawady, Sule Yildirim, Jon Yngve Hardeberg

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

Abstract

Textual keywords have been used in the early stages for image retrieval systems. Due to the huge increase of image content, an image is efficiently used instead according to the time computation. Deciding powerful feature representations are the important factors for the retrieval performance of a content-based image retrieval (CBIR) system. In this work, we present a combined feature representation based on handcrafted and deep approaches, to categorize editorial images into six classes (athletics, football, indoor, outdoor, portrait, ski). The experimental results show the superior performance of the combined features among different editorial classes.

Original language	English
Title of host publication	Image and Signal Processing
Subtitle of host publication	8th International Conference, ICISP 2018, Proceedings
Editors	Alamin Mansouri, Abderrahim El Moataz, Fathallah Nouboud, Driss Mammass
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	284-291
Number of pages	8
ISBN (Electronic)	9783319942117
ISBN (Print)	9783319942100
DOIs	https://doi.org/10.1007/978-3-319-94211-7_31
Publication status	Published - 30 Jun 2018
Externally published	Yes
Event	8th International Conference on Image and Signal Processing, ICISP 2018 - Cherbourg, France Duration: 2 Jul 2018 → 4 Jul 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10884 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Image and Signal Processing, ICISP 2018
Country/Territory	France
City	Cherbourg
Period	2/07/18 → 4/07/18

Keywords

BoVW
CBIR
CNN
image features
LBP
similarity

Access to Document

10.1007/978-3-319-94211-7_31

Cite this

Companioni-Brito, C., Elawady, M., Yildirim, S., & Hardeberg, J. Y. (2018). Editorial image retrieval using handcrafted and CNN features. In A. Mansouri, A. El Moataz, F. Nouboud, & D. Mammass (Eds.), Image and Signal Processing: 8th International Conference, ICISP 2018, Proceedings (pp. 284-291). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10884 LNCS). Springer. https://doi.org/10.1007/978-3-319-94211-7_31

Companioni-Brito, Claudia ; Elawady, Mohamed ; Yildirim, Sule et al. / Editorial image retrieval using handcrafted and CNN features. Image and Signal Processing: 8th International Conference, ICISP 2018, Proceedings. editor / Alamin Mansouri ; Abderrahim El Moataz ; Fathallah Nouboud ; Driss Mammass. Cham, Switzerland : Springer, 2018. pp. 284-291 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Editorial image retrieval using handcrafted and CNN features",

abstract = "Textual keywords have been used in the early stages for image retrieval systems. Due to the huge increase of image content, an image is efficiently used instead according to the time computation. Deciding powerful feature representations are the important factors for the retrieval performance of a content-based image retrieval (CBIR) system. In this work, we present a combined feature representation based on handcrafted and deep approaches, to categorize editorial images into six classes (athletics, football, indoor, outdoor, portrait, ski). The experimental results show the superior performance of the combined features among different editorial classes.",

keywords = "BoVW, CBIR, CNN, image features, LBP, similarity",

author = "Claudia Companioni-Brito and Mohamed Elawady and Sule Yildirim and Hardeberg, {Jon Yngve}",

year = "2018",

month = jun,

day = "30",

doi = "10.1007/978-3-319-94211-7_31",

language = "English",

isbn = "9783319942100",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "284--291",

editor = "Alamin Mansouri and {El Moataz}, Abderrahim and Fathallah Nouboud and Driss Mammass",

booktitle = "Image and Signal Processing",

note = "8th International Conference on Image and Signal Processing, ICISP 2018 ; Conference date: 02-07-2018 Through 04-07-2018",

}

Companioni-Brito, C, Elawady, M, Yildirim, S & Hardeberg, JY 2018, Editorial image retrieval using handcrafted and CNN features. in A Mansouri, A El Moataz, F Nouboud & D Mammass (eds), Image and Signal Processing: 8th International Conference, ICISP 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10884 LNCS, Springer, Cham, Switzerland, pp. 284-291, 8th International Conference on Image and Signal Processing, ICISP 2018, Cherbourg, France, 2/07/18. https://doi.org/10.1007/978-3-319-94211-7_31

Editorial image retrieval using handcrafted and CNN features. / Companioni-Brito, Claudia; Elawady, Mohamed; Yildirim, Sule et al.
Image and Signal Processing: 8th International Conference, ICISP 2018, Proceedings. ed. / Alamin Mansouri; Abderrahim El Moataz; Fathallah Nouboud; Driss Mammass. Cham, Switzerland: Springer, 2018. p. 284-291 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10884 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Editorial image retrieval using handcrafted and CNN features

AU - Companioni-Brito, Claudia

AU - Elawady, Mohamed

AU - Yildirim, Sule

AU - Hardeberg, Jon Yngve

PY - 2018/6/30

Y1 - 2018/6/30

N2 - Textual keywords have been used in the early stages for image retrieval systems. Due to the huge increase of image content, an image is efficiently used instead according to the time computation. Deciding powerful feature representations are the important factors for the retrieval performance of a content-based image retrieval (CBIR) system. In this work, we present a combined feature representation based on handcrafted and deep approaches, to categorize editorial images into six classes (athletics, football, indoor, outdoor, portrait, ski). The experimental results show the superior performance of the combined features among different editorial classes.

AB - Textual keywords have been used in the early stages for image retrieval systems. Due to the huge increase of image content, an image is efficiently used instead according to the time computation. Deciding powerful feature representations are the important factors for the retrieval performance of a content-based image retrieval (CBIR) system. In this work, we present a combined feature representation based on handcrafted and deep approaches, to categorize editorial images into six classes (athletics, football, indoor, outdoor, portrait, ski). The experimental results show the superior performance of the combined features among different editorial classes.

KW - BoVW

KW - CBIR

KW - CNN

KW - image features

KW - LBP

KW - similarity

U2 - 10.1007/978-3-319-94211-7_31

DO - 10.1007/978-3-319-94211-7_31

M3 - Conference contribution book

AN - SCOPUS:85049690737

SN - 9783319942100

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 284

EP - 291

BT - Image and Signal Processing

A2 - Mansouri, Alamin

A2 - El Moataz, Abderrahim

A2 - Nouboud, Fathallah

A2 - Mammass, Driss

PB - Springer

CY - Cham, Switzerland

T2 - 8th International Conference on Image and Signal Processing, ICISP 2018

Y2 - 2 July 2018 through 4 July 2018

ER -

Companioni-Brito C, Elawady M, Yildirim S, Hardeberg JY. Editorial image retrieval using handcrafted and CNN features. In Mansouri A, El Moataz A, Nouboud F, Mammass D, editors, Image and Signal Processing: 8th International Conference, ICISP 2018, Proceedings. Cham, Switzerland: Springer. 2018. p. 284-291. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-94211-7_31

Editorial image retrieval using handcrafted and CNN features

Abstract

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Keywords

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