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2021 | OriginalPaper | Buchkapitel

79. Image Retrieval Systems: From Underlying Feature Extraction to High Level Intelligent Systems

verfasst von : Shefali Dhingra, Poonam Bansal

Erschienen in: Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences

Verlag: Springer Singapore

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Abstract

In this digital era, the profound amounts of complex images are being produced due to the up gradation of image capturing devices. So there is a huge demand of an efficient retrieval system for indexing and retrieving these images. Content based image retrieval (CBIR) system has been an active and promising research field in the area of image retrieval and processing. This system aims at retrieving the most appropriate and visually similar images from the large databases with the extraction of low level features of the images like color, edge and texture by various extraction techniques. This paper analyzes the basic CBIR system and the various achievements obtained in these systems mainly in the areas of feature extraction, indexing and intelligent CBIR systems. The most of the research in this area is now being focussed in developing of an advanced and intelligent CBIR system by using various deep learning algorithms which includes Convolutional neural network, auto encoders; long short term neural networks etc. so that the accuracy of the system can be improved. Finally, in the paper our insights and challenges are also provided for future research.

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Literatur
1.
Zurück zum Zitat Wang H, Feng L, Zhang J, Liu Y (2016) Semantic discriminative metric learning for image similarity measurement. IEEE Trans Multimed 18(8):1579–1589CrossRef Wang H, Feng L, Zhang J, Liu Y (2016) Semantic discriminative metric learning for image similarity measurement. IEEE Trans Multimed 18(8):1579–1589CrossRef
2.
Zurück zum Zitat Alsmadi MK (2017) An efficient similarity measure for content based image retrieval using memetic algorithm. Egypt J Basic Appl Sci 4(2):112–122 Alsmadi MK (2017) An efficient similarity measure for content based image retrieval using memetic algorithm. Egypt J Basic Appl Sci 4(2):112–122
3.
Zurück zum Zitat Grigorova A, De Natale FGB, Dagli C, Huang TS (2007) Content-based image retrieval by feature adaptation and relevance feedback. IEEE Trans Multimed 9(6):1183–1192 Grigorova A, De Natale FGB, Dagli C, Huang TS (2007) Content-based image retrieval by feature adaptation and relevance feedback. IEEE Trans Multimed 9(6):1183–1192
4.
Zurück zum Zitat Wang J (2011) Bag-of-features based medical image retrieval via multiple assignment and visual words weighting. IEEE Trans Med Imaging 30(11):1996–2011CrossRef Wang J (2011) Bag-of-features based medical image retrieval via multiple assignment and visual words weighting. IEEE Trans Med Imaging 30(11):1996–2011CrossRef
5.
Zurück zum Zitat Lin C-H, Chen R-T, Chan Y-K (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27(6):658–665CrossRef Lin C-H, Chen R-T, Chan Y-K (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27(6):658–665CrossRef
6.
Zurück zum Zitat Reddy PVN, Prasad KS (2011) Color and texture features for content based image retrieval. Int J Comput Technol Appl 2:1016–1020 Reddy PVN, Prasad KS (2011) Color and texture features for content based image retrieval. Int J Comput Technol Appl 2:1016–1020
7.
Zurück zum Zitat Mistry Y, Ingole DT, Ingole MD (2017) Content based image retrieval using hybrid features and various distance metric. J Electr Syst Inf Technol Mistry Y, Ingole DT, Ingole MD (2017) Content based image retrieval using hybrid features and various distance metric. J Electr Syst Inf Technol
8.
Zurück zum Zitat Jenni K, Mandala S, Sunar MS (2015) Content based image retrieval using colour strings comparison. Procedia Comput Sci 50:374–379CrossRef Jenni K, Mandala S, Sunar MS (2015) Content based image retrieval using colour strings comparison. Procedia Comput Sci 50:374–379CrossRef
9.
Zurück zum Zitat Fadaei S, Amirfattahi R, Ahmadzadeh MR (2017) New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features. IET Image Process 11(2):89–98CrossRef Fadaei S, Amirfattahi R, Ahmadzadeh MR (2017) New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features. IET Image Process 11(2):89–98CrossRef
10.
Zurück zum Zitat Shriwas MK, Raut VR (2015) Content based image retrieval: a past, present and new feature descriptor. In: 2015 International conferences circuits, power computer technology [ICCPCT-2015], pp 1–7 Shriwas MK, Raut VR (2015) Content based image retrieval: a past, present and new feature descriptor. In: 2015 International conferences circuits, power computer technology [ICCPCT-2015], pp 1–7
11.
Zurück zum Zitat Vogel J, Schiele B (2006) Performance evaluation and optimization for content-based image retrieval. Pattern Recognit 39(5):897–909CrossRef Vogel J, Schiele B (2006) Performance evaluation and optimization for content-based image retrieval. Pattern Recognit 39(5):897–909CrossRef
12.
Zurück zum Zitat Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118MathSciNetCrossRef Liao S, Law MWK, Chung ACS (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118MathSciNetCrossRef
13.
Zurück zum Zitat Mohamadzadeh S, Farsi H (2013) Colour and texture feature-based image retrieval by using hadamard matrix in discrete wavelet transform. IET Image Process 7(3):212–218MathSciNetCrossRef Mohamadzadeh S, Farsi H (2013) Colour and texture feature-based image retrieval by using hadamard matrix in discrete wavelet transform. IET Image Process 7(3):212–218MathSciNetCrossRef
14.
Zurück zum Zitat Singh C, Kaur KP (2016) A fast and efficient image retrieval system based on color and texture features. J Vis Commun Image Recogn 41:225–238CrossRef Singh C, Kaur KP (2016) A fast and efficient image retrieval system based on color and texture features. J Vis Commun Image Recogn 41:225–238CrossRef
15.
Zurück zum Zitat Pavithra LK, Sharmila TS. (2017) An efficient framework for image retrieval using color, textureand edge features. J Comput Electr Eng 1–14 Pavithra LK, Sharmila TS. (2017) An efficient framework for image retrieval using color, textureand edge features. J Comput Electr Eng 1–14
16.
Zurück zum Zitat Mangijao S, Hemachandran K (2012) Content based image retrieval using color moment and gabor texture feature. Int J Comput Sci Issues 9:299–309 Mangijao S, Hemachandran K (2012) Content based image retrieval using color moment and gabor texture feature. Int J Comput Sci Issues 9:299–309
17.
Zurück zum Zitat Yasmin M, Mohsin S, Shariff M (2014) Intelligent image retrieval techniques: a survey. J Appl Res Technol 12(1):87–103CrossRef Yasmin M, Mohsin S, Shariff M (2014) Intelligent image retrieval techniques: a survey. J Appl Res Technol 12(1):87–103CrossRef
18.
Zurück zum Zitat Nikkam P, Eswara Reddy B (2017) An efficient approach for content based image retrieval using hierarchical part-template and tree modeling. J Image Video Process 8(2):1607–1613CrossRef Nikkam P, Eswara Reddy B (2017) An efficient approach for content based image retrieval using hierarchical part-template and tree modeling. J Image Video Process 8(2):1607–1613CrossRef
19.
Zurück zum Zitat Dhingra S, Bansal P (2019) A competent and novel approach of designing an intelligent image retrieval system. EAI Trans Scalable Inf Syst 7(24):1–15 Dhingra S, Bansal P (2019) A competent and novel approach of designing an intelligent image retrieval system. EAI Trans Scalable Inf Syst 7(24):1–15
20.
Zurück zum Zitat Liu F, Wang Y, Wang F, Chang Y, Lin J (2019) Intelligent and secure content-based image retrieval for mobile users. IEEE Trans 4:1–14CrossRef Liu F, Wang Y, Wang F, Chang Y, Lin J (2019) Intelligent and secure content-based image retrieval for mobile users. IEEE Trans 4:1–14CrossRef
23.
Zurück zum Zitat Mosbah M, Boucheham B (2017) Distance selection based on relevance feedback in the context of CBIR using the SFS meta-heuristic with one round. Egyptian Inform J 18(1):1–9CrossRef Mosbah M, Boucheham B (2017) Distance selection based on relevance feedback in the context of CBIR using the SFS meta-heuristic with one round. Egyptian Inform J 18(1):1–9CrossRef
24.
Zurück zum Zitat Zhou Z, Chen K, Dai H (2006) Enhancing relevance feedback in image retrieval using unlabeled data. ACM Trans Inf Syst 24(2):219–244 Zhou Z, Chen K, Dai H (2006) Enhancing relevance feedback in image retrieval using unlabeled data. ACM Trans Inf Syst 24(2):219–244
Metadaten
Titel
Image Retrieval Systems: From Underlying Feature Extraction to High Level Intelligent Systems
verfasst von
Shefali Dhingra
Poonam Bansal
Copyright-Jahr
2021
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-7533-4_79

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