SpringerLink
Log in

RETRACTED ARTICLE: Essentiality for bridging the gap between low and semantic level features in image retrieval systems: an overview

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

This article was retracted on 04 July 2022

This article has been updated

Abstract

The modern world uses digital data invariably and the advent of smart phones and cameras add to images and communicating through images in social media is a very common happening due to the exposure to internet and the economical availability of data. Moreover, people explore the web for much news and on different topics. They feel satisfied to look into image type info than textual information. So, image retrieval is paramount nowadays and it is very much useful in many societal and defence applications. The methods used for retrieval started through text based and query based is popularly used now. The content based retrieval uses many stages, approaches, technologies, algorithms etc. It is intended to provide a broad perspective about CBIR in this work. Moreover, the computational and time complexities involved during retrieval relies on the performance of the entire system. One of the aspects that can improve the precision is the reduction of semantic gap. Taking this as the base, we have explored a literature and presented the points related to various points including relevance feedback so as to enable other researchers to carry out experimental work on the suggested areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: adopted from (Junior et al. 2014)

Fig. 2

Source: adopted from (Singh et al. 2012)

Fig. 3

Source: adopted from (Singh et al. 2012)

Similar content being viewed by others

Change history

References

  • Aghav-Palwe S, Mishra D (2018) Color image retrieval using compacted feature vector with mean-count tree. Proced Comput Sci 132:1739–1746. https://doi.org/10.1016/j.procs.2018.05.149

    Article  Google Scholar 

  • Akhilesh K, ShedamkarSharma RRS (2016) Comparison and analysis of different image retrieval systems. Int J Recent Trends Eng Res 2:211–222

    Google Scholar 

  • Ali A, Sharma S (2017) Content based image retrieval using feature extraction with machine learning. In: 2017 International conference on intelligent computing and control systems (ICICCS). IEEE, pp 1048–1053

  • Ali N, Bajwa KB, Sablatnig R et al (2016) A novel image retrieval based on visual words integration of SIFT and SURF. PLoS ONE 11:e0157428. https://doi.org/10.1371/journal.pone.0157428

    Article  Google Scholar 

  • Ali N, Ali Mazhar D, Iqbal Z, et al (2017) Content-based image retrieval based on late fusion of binary and local descriptors

  • Azodinia M, Hajdu A (2016) A novel combinational relevanfile:///C:/Users/Public/Desktop/WinZip.lnkce feedback based method for content-based image retrieval short-term learning methods. Acta Polytech Hungar 13:121–134

    Google Scholar 

  • Bakar SA, Hitam MS, Wan Yussof WNJH (2013) Content-based image retrieval using SIFT for binary and greyscale images. In: 2013 IEEE international conference on signal and image processing applications. IEEE, pp 83–88

  • Bella MIt, Vasuki A (2019) An efficient image retrieval framework using fused information feature. Comput Electr Eng 75:46–60. https://doi.org/10.1016/j.compeleceng.2019.01.022

    Article  Google Scholar 

  • Cho SJ, Yoo SI (2006) A matching algorithm for content-based image retrieval. Seoul National University, Seoul

    Google Scholar 

  • Ciocca G, Napoletano P, Schettini R (2018) CNN-based features for retrieval and classification of food images. Comput Vis Image Underst 176–177:70–77. https://doi.org/10.1016/j.cviu.2018.09.001

    Article  Google Scholar 

  • Júnior JA da S, Marçal RE, Batista MA (2014) Image retrieval: importance and applications. In: X Work. Vis˜ao Comput. https://pdfs.semanticscholar.org/ebd5/da27f2ea342227b3685b34cdaa8fe9dd4847.pdf. Accessed 12 Mar 2019

  • Dorko G, Schmid C (2004) Object class recognition using discriminative local features. IEEE Trans Pattern Anal Mach Intell 2:1–26

    Google Scholar 

  • Gali R, Dewal ML, Anand RS (2012) Genetic algorithm for content based image retrieval. In: 2012 fourth international conference on computational intelligence, communication systems and networks. IEEE, pp 243–247

  • Guan R, Wang X, Marchese M et al (2019) Feature space learning model. J Ambient Intell Human Comput 10:2029–2040. https://doi.org/10.1007/s12652-018-0805-4

    Article  Google Scholar 

  • Hassaballah M, Abdelmgeid AA, Alshazly HA (2016) Image features detection, description and matching. In: Awad AI, Hassaballah M (eds) Image feature detectors and descriptors; foundations and applications. Springer, Berlin, pp 11–45

    Chapter  Google Scholar 

  • Huang C, Wang G (2006) Method of image retrieval based on color coherence vector

  • Jabeen S, Mehmood Z, Mahmood T et al (2018) An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model. PLoS ONE 13:e0194526. https://doi.org/10.1371/journal.pone.0194526

    Article  Google Scholar 

  • Jadhav SM, Patil V (2012) An effective content based image retrieval (CBIR) system based on evolutionary programming (EP). In: 2012 IEEE international conference on advanced communication control and computing technologies (ICACCCT). IEEE, pp 310–315

  • Jain A, Muthuganapathy R, Ramani K (2007) Content-based image retrieval using shape and depth from an engineering database. Adv Vis Comput. https://doi.org/10.1007/978-3-540-76856-2_25

    Article  Google Scholar 

  • Jaworska T (2013) Fuzzy rule-based classifier for content-based image retrieval, pp 3–13

  • Jian M, Guo H, Liu L (2009) Texture image classification using visual perceptual texture features and gabor wavelet features. J Comput 4:763–770. https://doi.org/10.4304/jcp.4.8.763-770

    Article  Google Scholar 

  • Jiang W, Chan KL, Li M, Hongjiang Zhang (2005) Map** low-level features to high-level semantic concepts in region-based image retrieval. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). IEEE, pp 244–249

  • Junior JADS, Marcal RE, Batista MA (2014) Image retrieval: importance and applications. Work Visao Comput 20:311–315

    Google Scholar 

  • Kavitha H, Sudhamani MV (2016) Content-based image retrieval using edge and gradient orientation features of an object in an image from database. J Intell Syst. https://doi.org/10.1515/jisys-2014-0088

    Article  Google Scholar 

  • Kekre HB, Thepade SarodeanujaSuryawanshi SDKV (2013) Image retrieval using texture features extracted from GLCM, LBG and KPE. Int J Comput Theory Eng 2:695–700. https://doi.org/10.7763/ijcte.2010.v2.227

    Article  Google Scholar 

  • Khatua CK, Nayak SK, Panda CS (2011) Content based image retrieval using fuzzy color histogram, p 4

  • Kiran M, Ahmed I, Khan N et al (2019) Chest X-ray segmentation using Sauvola thresholding and Gaussian derivatives responses. J Ambient Intell Human Comput 10:4179–4195. https://doi.org/10.1007/s12652-019-01281-7

    Article  Google Scholar 

  • Kokare M, Chatterji BN, Biswas PK (2003) Comparison of similarity metrics for texture image retrieval. In: TENCON 2003. conference on convergent technologies for Asia-Pacific Region. Allied Publishers Pvt. Ltd, pp 571–575

  • Kokare M, Biswas PK, Chatterji BN (2007) Texture image retrieval using rotated wavelet filters. Pattern Recognit Lett 28:1240–1249. https://doi.org/10.1016/j.patrec.2007.02.006

    Article  Google Scholar 

  • Kumar K (2010) CBIR: content based image retrieval. In: National conference on advances in information security(NCAIS-2010), pp 1–8

  • Kumar S, Shukla AK (2017) Design and analysis of CBIR system using hybrid PSO and K-mean clustering methods. Int J Curr Eng Technol 7:397–401

    Google Scholar 

  • Kumar R, Singh BK (2018) Performance evaluation of invariant moment features on image retrieval. Int J Comput Sci Eng 5:73–78. https://doi.org/10.26438/ijcse/v5i12.7378

    Article  Google Scholar 

  • Lingadalli RK, Ramesh N (2015) Content based image retrieval using color, shape and texture. Int Adv Res J Sci Eng Technol 2:40–45. https://doi.org/10.17148/IARJSET.2015.2610

    Article  Google Scholar 

  • Lisin DA, Mattar MA, Blaschko MB, et al (2005) Combining local and global image features for object class recognition. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05)-workshops. IEEE, pp 47–47

  • Liu P, Guo J-M, Wu C-Y, Cai D (2017) Fusion of deep learning and compressed domain features for content-based image retrieval. IEEE Trans Image Process 26:5706–5717. https://doi.org/10.1109/TIP.2017.2736343

    Article  MathSciNet  MATH  Google Scholar 

  • Mai NTL, Ridzuan SSBA, Bin OZ (2018) Content-based image retrieval system for an image gallery search application. Int J Electr Comput Eng 8:1903. https://doi.org/10.11591/ijece.v8i3.pp1903-1912

    Article  Google Scholar 

  • Meng F, Shan D, Shi R et al (2018) Merged region based image retrieval. J Vis Commun Image Represent 55:572–585. https://doi.org/10.1016/j.jvcir.2018.07.003

    Article  Google Scholar 

  • Mistry Y, Ingole DT, Ingole MD (2018) Content based image retrieval using hybrid features and various distance metric. J Electr Syst Inf Technol 5:874–888. https://doi.org/10.1016/j.jesit.2016.12.009

    Article  Google Scholar 

  • Murphy K, Torralba A, Eaton D, Freeman W (2006) Object detection and localization using local and global features, pp 382–400

  • Nair LR, Subramaniam K, Venkatesan GKDP (2020a) An effective image retrieval system using machine learning and fuzzy c-means clustering approach. Multimed Tools Appl 79:10123–10140. https://doi.org/10.1007/s11042-019-08090-2

    Article  Google Scholar 

  • Nair LR, Subramaniam K, Prasannavenkatesan GKD (2020b) A review on multiple approaches to medical image retrieval system. In: Solanki V, Hoang M, Lu Z, Pattnaik P (eds) Intelligent computing in engineering. Advances in intelligent systems and computing, vol 1125. Springer, Singapore

    Google Scholar 

  • Naz S, Iqbal A, Imran M et al (2016) Content-based image retrieval using texture color shape and region. Int J Adv Comput Sci Appl 7:418–426. https://doi.org/10.14569/ijacsa.2016.070156

    Article  Google Scholar 

  • Nidhyananthan S (2007) Image retrieval using shape feature

  • Pandey D, Kushwah S (2016) A review on CBIR with its advantages and disadvantages for low-level features. Int J Comput Sci Eng 4:161–167. https://doi.org/10.9734/bjmcs/2016/24000

    Article  Google Scholar 

  • Rahmani MKI, Ansari MA, Goel AK (2015) An efficient indexing algorithm for CBIR. In: 2015 IEEE international conference on computational intelligence and communication technology. IEEE, pp 73–77

  • Rose RIH, Subha**i AC (2017) Multiple class-association rules for content based image retrieval with efficiency. Int J Pure Appl Math 116:375–385

    Google Scholar 

  • Sharma A, Singh G (2013) Comparative study: content based image retrieval using low level features. Int J Eng Res Appl 3:962–967

    Google Scholar 

  • Sharma DK, Pamula R, Chauhan DS (2019) A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01247-9

    Article  Google Scholar 

  • Singaravelan S, Murugan D, Rajalakshmi K, Santhiya G (2015) Refining CBIR using rule based KNN. J Remote Sens Technol. https://doi.org/10.18005/jrst0101003

    Article  Google Scholar 

  • Singh SM, Hemachandran K (2010) Content-based image retrieval using color moment and gabor texture feature. Int Conf Mach Learn Cybern ICMLC 2:719–724. https://doi.org/10.1109/ICMLC.2010.5580566

    Article  Google Scholar 

  • Singh N, Dubey SR, Dixit P, Gupta JP (2012) Semantic image retrieval using multiple features. In: Computer science and information technology (CS & IT). Academy & Industry Research Collaboration Center (AIRCC), pp 277–284

  • Somnugpong S, Khiewwan K (2016) Content-based image retrieval using a combination of color correlograms and edge direction histogram. In: 2016 13th international joint conference on computer science and software engineering (JCSSE). IEEE, pp 1–5

  • Syam B, Victor JSR, Rao YS (2013) Efficient similarity measure via Genetic algorithm for content based medical image retrieval with extensive features. In: 2013 international mutli-conference on automation, computing, communication, control and compressed sensing (iMac4s). IEEE, pp 704–711

  • Tamilkodi R, Karthika RA, RoslineNesaKumari G, Maruthuperumal S (2015) Segment based image retrieval using HSV color space and moment, pp 239–247

  • Vatamanu OA, Frandeş M, Lungeanu D, Mihalaş G-I (2015) Content based image retrieval using local binary pattern operator and data mining techniques. Stud Health Technol Inform 210:75–79

    Google Scholar 

  • Venkata G, Reddy R, Vijaya Kumar V, Birudu S (2018) A novel texture synthesis algorithm using patch matching by fuzzy texture unit

  • Vijayakumar P, Abhishek R, Sandeep K (2016) Hybrid classifier based content based image retrieval. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i46/91831

    Article  Google Scholar 

  • Vimina ER, Poulose Jacob K (2012) Image retrieval using low level features of object regions with application to partially occluded images. In: Progress in pattern recognition, image analysis, computer vision, and applications. CIARP 2012. lecture notes in computer science. Springer, Berlin, pp 422–429

  • Wen H, Zhan Y (2017) Content-based image retrieval base on relevance feedback, p 020039

  • Wilson J, Arif M (2017) Scene recognition by combining local and global image descriptors. https://arxiv.org/pdf/1702.06850.pdf. Accessed 28 Mar 2019

  • Xu K, Liu J, Miao J et al (2019) An improved SIFT algorithm based on adaptive fractional differential. J Ambient Intell Human Comput 10:3297–3305. https://doi.org/10.1007/s12652-018-1055-1

    Article  Google Scholar 

  • Yue J, Li Z, Liu L, Fu Z (2011) Content-based image retrieval using color and texture fused features. Math Comput Model 54:1121–1127. https://doi.org/10.1016/j.mcm.2010.11.044

    Article  Google Scholar 

  • M Zakariya S, Ali R, Ahmad N (2010) Unsupervised content based image retrieval by combining visual features of an image with a threshold

  • Zhang C, Huang L (2014) Content-based image retrieval using multiple features. J Comput Inf Technol 22:1. https://doi.org/10.2498/cit.1002256

    Article  Google Scholar 

  • Zhang H, Su Z (2002) Relevance feedback in CBIR. Visual and multimedia information management. Springer, Boston, pp 21–35

    Chapter  Google Scholar 

  • Zhang D, Wong A, Indrawan-Santiago M, Lu G (2000) Content-based image retrieval using Gabor texture features

  • Zhou Z-H, Chen K-J, Dai H-B (2006) Enhancing relevance feedback in image retrieval using unlabeled data. ACM Trans Inf Syst 24:219–244. https://doi.org/10.1145/1148020.1148023

    Article  Google Scholar 

  • Zhou J, Fu H, Kong X (2011) A balanced semi-supervised hashing method for CBIR

  • Zhu J, Rizzo J-R, Fang Y (2019) Learning domain-invariant feature for robust depth-image-based 3D shape retrieval. Pattern Recognit Lett 119:24–33. https://doi.org/10.1016/j.patrec.2017.09.041

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India

    Lakshmi R. Nair & Kamalraj Subramaniam

  2. Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India

    G. K. D. PrasannaVenkatesan

  3. Department of CSE, Karpagam College of Engineering, Coimbatore, Tamilnadu, India

    P. S. Baskar

  4. Department of ECE, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, Tamilnadu, India

    T. Jayasankar

Authors
  1. Lakshmi R. Nair
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamalraj Subramaniam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. K. D. PrasannaVenkatesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. S. Baskar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T. Jayasankar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lakshmi R. Nair.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04285-y

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nair, L.R., Subramaniam, K., PrasannaVenkatesan, G.K.D. et al. RETRACTED ARTICLE: Essentiality for bridging the gap between low and semantic level features in image retrieval systems: an overview. J Ambient Intell Human Comput 12, 5917–5929 (2021). https://doi.org/10.1007/s12652-020-02139-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02139-z

Keywords

Search

Navigation