Top

Pattern Recognition and Image Analysis

Published in:

01-10-2021 | ARTIFICIAL INTELLIGENCE TECHNIQUES IN PATTERN RECOGNITION AND IMAGE ANALYSIS

Investigation of an Efficient Integrated Semantic Interactive Algorithm for Image Retrieval

Authors: Thusnavis Bella Mary I, P. Malin Bruntha, M. A. P. Manimekalai, K. Martin Sagayam, Hien Dang

Published in: Pattern Recognition and Image Analysis | Issue 4/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this research, a novel integrated semantic interactive algorithm for image retrieval is proposed to retrieve set of relevant images for a given query. The main challenge in image retrieval is to retrieve relevant images with high precision and to diminish the semantic gap. Ranking relevance feedback, semantic feature template, and unsupervised cluster learning techniques are the three approaches investigated to meet the challenge in image retrieval. In this paper, integrated ranking relevance feedback–semantic feature template and integrated ranking relevance feedback–unsupervised cluster learning is proposed to increase the retrieval rate. The experimental results are explored with Corel-1K image database and are evaluated using precision, recall, error rate and execution time to demonstrate the effectiveness of the proposed system. The simulation results reveal that the integrated ranking relevance feedback–semantic feature template algorithm yields high precision and outperforms the other existing methods with the maximum precision.

previous article Using Hough-Like Transforms for Extracting Relevant Regularities from Big Applied Data

next article An Attention Hierarchical Topic Modeling

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

A. Alzu’bi, A. Amira, and N. Ramzan, “Semantic content-based image retrieval: A comprehensive study,” J. of Visual Commun. Image Representation 32, 20–54 (2015). https://doi.org/10.1016/j.jvcir.2015.07.012CrossRef

K. T. Ahmed, S. Ummesafi, and A. Iqbal, “Content based image retrieval using image features information fusion,” Inf. Fusion 51, 76–99 (2019). https://doi.org/10.1016/j.inffus.2018.11.004CrossRef

A. Grigorova, F. G. B. De Natale, Ch. Dagli, and T. S. Huang, “Content based image retrieval by feature adaptation and relevance feedback,” IEEE Trans. Multimedia 9, 1183–1192 (2007). https://doi.org/10.1109/TMM.2007.902828CrossRef

S. Ansa, I. Thusnavis Bella Mary, A. Vasuki, and P. S. Lakshmi, “Reduction of semantic gap using relevance feedback technique in image retrieval system,” The Fifth Int. Conf. on the Applications of Digital Information and Web Technologies, Bangalore, India, 2014 (IEEE, 2014), pp. 148–153. https://doi.org/10.1109/ICADIWT.2014.6814677

A. K. Bedi and R. K. Sunkaria, “Local tetra-directional pattern – A new texture descriptor for content-based image retrieval,” Pattern Recognit. Image Anal. 30, 578–592 (2020). https://doi.org/10.1134/S1054661820040057CrossRef

R. P. Bohush, S. V. Ablameyko, E. R. Adamovskiy, and D. Savca, “Image similarity estimation based on ratio and distance calculation between features,” Pattern Recognit. Image Anal. 30, 147–159 (2020). https://doi.org/10.1134/S1054661820020030CrossRef

T. Boomilingam, and M. Subramaniam, “An efficient retrieval using edge GLCM and association rule mining guided IPSO based artificial neural network,” Multimedia Tools Appl. 76, 21729–21747 (2017). https://doi.org/10.1007/s11042-016-3969-yCrossRef

C. Singh and Pooja, “Improving image retrieval using combined features of Hough transform and Zernike moments,” Opt. Lasers Eng. 49, 1384–1396 (2011). https://doi.org/10.1016/j.optlaseng.2011.07.009CrossRef

E. Cheng, F. Jing, and L. Zhang, “A unified relevance feedback framework for web image retrieval,” IEEE Trans. Image Process. 18, 1350–1357 (2009). https://doi.org/10.1109/TIP.2009.2017128MathSciNetCrossRefMATH

10.

G. Ciocca, C. Cusano, S. Santini, and R. Schettini, “Halfway through the semantic gap: Prosemantic features for image retrieval,” Inf. Sci. 181, 4943–4958 (2011). https://doi.org/10.1016/j.ins.2011.06.025CrossRef

11.

H. Dang, K. M. Sagayam, P. M. Bruntha, S. Dhanasekar, A. A. A. Jone, and G. Rajesh, “Image fusion based on sparse sampling method and hybrid discrete cosine transformation,” Int. J. Sci. Technol. Res. 8, 1103–1107 (2019).

12.

D. Cerra and M. Datcu, “A fast compression-based similarity measure with applications to content based image retrieval,” J. Visual Commun. Image Representation 23, 293–302 (2012). https://doi.org/10.1016/j.jvcir.2011.10.009CrossRef

13.

E. de Ves, X. Benavent, I. Coma, and G. Ayala, “A novel dynamic multi-model relevance feedback procedure for content based image retrieval,” Neurocomputing 208, 99–107 (2016). https://doi.org/10.1016/j.neucom.2016.02.073CrossRef

14.

E. Walia and A. Pal, “Fusion framework for effective color image retrieval,” J. Visual Commun. Image Representation 25, 1335–1348 (2014). https://doi.org/10.1016/j.jvcir.2014.05.005CrossRef

15.

E. Santos, Jr., and Q. Gu, “Content based image retrieval using semantic analysis,” J. Intell. Inf. Syst. 43, 247–269 (2014). https://doi.org/10.1007/s10844-014-0321-8CrossRef

16.

M. Ferecatu, N. Boujemaa, and M. Crucianu, “Semantic interactive image retrieval combining visual and conceptual content description,” Multimedia Syst. 13, 309–322 (2008). https://doi.org/10.1007/s00530-007-0094-9CrossRef

17.

G.-H. Liu and J.-Y. Yang, “Content-based image retrieval using color difference histogram,” Pattern Recognit. 46, 188–198 (2013). https://doi.org/10.1016/j.patcog.2012.06.001CrossRef

18.

G.-T. Zhou, K. M. Ting, F. T. Liu, and Y. Yin, “Relevance feature mapping for content-based multimedia information retrieval,” Pattern Recognit. 45, 1707–1720 (2012). https://doi.org/10.1016/j.patcog.2011.09.016CrossRef

19.

K. Halavataya, “Local feature descriptor indexing for image matching and object detection in real time applications,” Pattern Recognit. Image Anal. 30, 16–21 (2020). https://doi.org/10.1134/S105466182001006XCrossRef

20.

H. Müller, W. Müller, D. McG. Squire, S. Marchand-Maillet, and T. Pun, “Performance evaluation in content-based image retrieval: overview and proposals,” Pattern Recognit. Lett. 22, 593–601 (2001). https://doi.org/10.1016/S0167-8655(00)00118-5CrossRefMATH

21.

M. Huang, H. Shu, Y. Ma, and Q. Gong, “Content based image retrieval technology using multi-feature fusion,” Optik – Int. J. Light Electron Opt. 126, 2144–2148 (2015). https://doi.org/10.1016/j.ijleo.2015.05.095CrossRef

22.

V. Jindal, S. Bawa, and S. Batra, “A review of ranking approaches for semantic search on Web,” Inf. Process. Manage. 50, 416–425 (2014). https://doi.org/10.1016/j.ipm.2013.10.004CrossRef

23.

J. Li and N. M. Allinson, “Relevance feedback in content-based image retrieval: A survey,” in Handbook on Neural Information Processing, Ed. by M. Bianchini, M. Maggini, and L. Jain, Intelligent Systems Reference Library, vol. 49 (Springer, Berlin, 2013), pp. 433–469. https://doi.org/10.1007/978-3-642-36657-4_13

24.

H. Karamti, M. Tmar, M. Visani, T. Urruty, and F. Gargouri, “Vector space model adaptation and pseudo relevance feedback for content based image retrieval,” Multimedia Tools Appl. 77, 5475–5501 (2018). https://doi.org/10.1007/s11042-017-4463-xCrossRef

25.

A. Khokher and R.Talwar, “A fast and effective image retrieval scheme using color-texture-and shape based hisograms,” Multimedia Tools Appl. 76, 21787–21809 (2017). https://doi.org/10.1007/s11042-016-4096-5CrossRef

26.

Y.-H. Kuo, W.-H. Cheng, H.-T. Lin, and W. H. Hsu, “Unsupervised semantic feature discovery for image object retrieval and tag refinement. IEEE Trans. multimedia,” IEEE Trans. Multimedia 14, 1079–1089 (2012). https://doi.org/10.1109/TMM.2012.2190386CrossRef

27.

C.-H. Lin, R.-T. Chen, and Y.-K. Chan, “A smart content-based image retrieval system based on color and texture feature,” Image Vision Comput. 27, 658–665 (2009). https://doi.org/10.1016/j.imavis.2008.07.004CrossRef

28.

L. Zhang, H. P. H. Shum, and L. Shao, “Discriminative semantic subspace analysis for relevance feedback,” IEEE Trans. Image Process. 25, 1275–1287 (2016).MathSciNet

29.

W.-C. Lin, Z.-Y. Chen, S.-W. Ke, C.-F. Tsai, and W.‑Y. Lin, “The effect of low-level image features on pseudo relevance feedback,” Neurocomputing 166, 26–37 (2015). https://doi.org/10.1016/j.neucom.2015.04.037CrossRef

30.

Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, “A survey of content-based image retrieval with high-level semantics,” Pattern Recognit. 40, 262–282 (2007). https://doi.org/10.1016/j.patcog.2006.04.045CrossRefMATH

31.

P. Luca and G. Giacinto, “Information fusion in content-based image retrieval: A comprehensive overview,” Inf. Fusion 37, 50–60 (2017). https://doi.org/10.1016/j.inffus.2017.01.003CrossRef

32.

H. Ma, J. Zhu, M. R.-T. Lyu, and I. King, “Bridging the semantic gap between image contents and tags,” IEEE Trans. Multimedia 12, 462–473 (2010). https://doi.org/10.1109/TMM.2010.2051360CrossRef

33.

M. Fakheri, T. Sedghi, M. G. Shayesteh, and M. C. Amirani, “Framework for image retrieval using machine learning and statistical similarity matching techniques,” IET Image Process. 7, 1–11 (2013). https://doi.org/10.1049/iet-ipr.2012.0104CrossRef

34.

M. K. Kundu, M. Chowdhury, and S. R. Bulò, “A graph-based relevance feedback mechanism in content-based image retrieval,” Knowl.-Based Syst. 73, 254–264 (2015). https://doi.org/10.1016/j.knosys.2014.10.009CrossRef

35.

M. Verma, B. Raman, and S. Murala, “Local extrema co-occurrence pattern for color and texture image retrieval,” Neurocomputing 165, 255–269 (2015). https://doi.org/10.1016/j.neucom.2015.03.015CrossRef

36.

M. Arevalillo-Herráez and F. J. Ferri, “An improved distance-based relevance feedback strategy for image retrieval,” Image Vision Comput. 31, 704–713 (2013). https://doi.org/10.1016/j.imavis.2013.07.004CrossRef

37.

M. Yasmin, S. Mohsim, and M. Sharif, “Intelligent image retrieval techniques: A survey,” J. Appl. Res. Technol. 12, 87–103 (2014). https://doi.org/10.1016/S1665-6423(14)71609-8CrossRef

38.

N. Shrivastava and V. Tyagi, “An efficient technique for retrieval of color images in large databases,” Comput. Electr. Eng. 46, 314–327 (2015). https://doi.org/10.1016/j.compeleceng.2014.11.009CrossRef

39.

L. K. Pavithra and T. S. Sharmila., “An efficient framework for image retrieval using color, texture and edge features,” Comput. Electr. Eng. 70, 580–593 (2018). https://doi.org/10.1016/j.compeleceng.2017.08.030CrossRef

40.

T. Qin, X.-D. Zhang, T.-Y. Liu, D.-S. Wang, W.‑Y. Ma, and H.-J. Zhang, “An active feedback framework for image retrieval,” Pattern Recognit. Lett. 29, 637–646 (2008). https://doi.org/10.1016/j.patrec.2007.11.015CrossRef

41.

S. R. Bulò, M. Rabbi, and M. Pelillo, “Content-based image retrieval with relevance feedback using random walks,” Pattern Recognit. 44, 2109–2122 (2011). https://doi.org/10.1016/j.patcog.2011.03.016CrossRef

42.

J. F. Serrano-Talamantes, C. Avilés-Cruz, J. Villegas-Cortez, and J. H. Sossa-Azuela, “Self-organizing natural scene image retrieval,” Experts Syst. Appl. 40, 2398–2409 (2013). https://doi.org/10.1016/j.eswa.2012.10.064CrossRef

43.

S. Pandey, P. Khanna, and H. Yokota, “Clustering of hierarchical image database to reduce inter and intra semantic gaps in visual space for finding specific image semantics,” J. Visual Commun. Image Representation 38, 704–720 (2015). https://doi.org/10.1016/j.jvcir.2016.04.013CrossRef

44.

S. Pandey, P. Khanna, and H. Yokota, “An effective use of adaptive combination of visual features to retrieve image semantics from a hierarchical image database,” J. Visual Commun. Image Representation 30, 136–152 (2015). https://doi.org/10.1016/j.jvcir.2015.03.010CrossRef

45.

S. Pandey, P. Khanna, and H. Yokota, “A semantics and image retrieval system for hierarchical image databases,” Inf. Process. Manage. 52, 571–591 (2016). https://doi.org/10.1016/j.ipm.2015.12.005CrossRef

46.

S. C. H. Hoi, R. M. Lyu, and R. Jin, “A unified log-based relevance feedback scheme for image retrieval,” IEEE Trans. Knowl. Data Eng. 18, 509–524 (2006). https://doi.org/10.1109/TKDE.2006.1599389CrossRef

47.

N. Suditu and F. Fleuret, “Adaptive relevance feedback for large scale image,” Multimedia Tools Appl. 75, 6777–6807 (2016). https://doi.org/10.1007/s11042-015-2610-9CrossRef

48.

J.-H. Su, W.-J. Huang, P. S. Yu, and V. S. Tseng, “Efficient relevance feedback for content-based image retrieval by mining user navigation patterns,” IEEE Trans. Knowl. Data Eng. 23, 360–372 (2011). https://doi.org/10.1109/TKDE.2010.124CrossRef

49.

I. M. Thusnavis Bella and A. Vasuki, “An efficient image retrieval framework using fused information feature,” Computers Electr. Eng. 75, 46–60 (2019). https://doi.org/10.1016/j.compeleceng.2019.01.022CrossRef

50.

R. T. Calumby, M. A. Gonçalves, and R. da Silva Torres, “On interactive learning-to-rank for IR: Overview, recent advances, challenges, and directions,” Neurocomputing 208, 3–24 (2016). https://doi.org/10.1016/j.neucom.2016.03.084CrossRef

51.

A. Vailaya, M. A. T. Figueiredo, A. K. Jain, and H.‑J. Zhang, “Image classification for content-based indexing,” IEEE Trans. Image Process. 10, 117–130 (2001). https://doi.org/10.1109/83.892448CrossRefMATH

52.

E. de Ves, G. Ayala, X. Benavent, J. Domingo, and E. Dura, “Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap,” Neurocomputing 168, 829–845 (2015). https://doi.org/10.1016/j.neucom.2015.05.041CrossRef

53.

H. Wei, Y. Gao, and K. L. Chan, “A review of region-based image retrieval,” J. Signal Process. Syst. 59, 143–161 (2010). https://doi.org/10.1007/s11265-008-0294-3CrossRef

54.

J. Wu, H. Shen, Y.-D. Li, Z.-B. Xiao, M.-Y. Lu, and C.-L. Wang, “Learning a hybrid similarity measure for image retrieval,” Pattern Recognit. 46, 2927–2939 (2013). https://doi.org/10.1016/j.patcog.2013.04.008CrossRefMATH

55.

X.-Y. Wang, Y.-W. Li, H.-Y. Yang, and J.-W. Chen, “An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification,” Neurocomputing 127, 214–230 (2014). https://doi.org/10.1016/j.neucom.2013.08.007CrossRef

56.

X.-Y. Wang, H.-Y. Yang, Y.-W. Li, W.-Y. Li, and J.‑W. Chen, “A new SVM-based active feedback scheme for image retrieval,” Eng. Appl. Artif. Intell. 37, 43–53 (2015). https://doi.org/10.1016/j.engappai.2014.08.012CrossRef

57.

X.-Y. Wang, L.-L. Liang, W.-Y. Li, D.-M. Li, and H.‑Y. Yang, “A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function,” J. Visual Commun. Image Representation 38, 256–275 (2016). https://doi.org/10.1016/j.jvcir.2016.03.008CrossRef

58.

W. Xiang-Yang, Z. Bei-Bei, and H.-Y. Yang, “Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting,” Eng. Appl. Artif. Intell. 26, 368–381 (2013). https://doi.org/10.1016/j.engappai.2012.05.008CrossRef

59.

X.-Y. Wang, J-W. Chen, and H.-Y. Yang, “A new integrated SVM classifiers for relevance feedback content-based image retrieval using EM parameter estimation,” Appl. Soft Comput. 11, 2787–2804 (2011). https://doi.org/10.1016/j.asoc.2010.11.009CrossRef

60.

Y. Zhu, J. Jiang, W. Han, Y. Ding, and Q. Tian, “Interpretation of users’ feedback via swarmed particles for content-based image retrieval,” Inf. Sci. 375, 246–257 (2017). https://doi.org/10.1016/j.ins.2016.09.021CrossRef

61.

M. Zhang, K. Zhang, Q. Feng, J. Wang, J. Kong, and Y. Lu, “A novel image retrieval method based on hybrid information descriptors,” J. Visual Commun. Image Representation 25, 1574–1587 (2014). https://doi.org/10.1016/j.jvcir.2014.06.016CrossRef

62.

Z. Xiao and X. Qi, “Complementary relevance feedback-based content-based image retrieval,” Multimedia Tools Appl. 73, 2157–2177 (2014). https://doi.org/10.1007/s11042-013-1693-4CrossRef

63.

Z. U. Qayyum, “Image retrieval through qualitative representations over semantic features,” Multimedia Tools Appl. 74, 1935–1959 (2015). https://doi.org/10.1007/s11042-013-1731-2CrossRef

Title: Investigation of an Efficient Integrated Semantic Interactive Algorithm for Image Retrieval
Authors: Thusnavis Bella Mary I
P. Malin Bruntha
M. A. P. Manimekalai
K. Martin Sagayam
Hien Dang
Publication date: 01-10-2021
Publisher: Pleiades Publishing
Published in: Pattern Recognition and Image Analysis / Issue 4/2021
Print ISSN: 1054-6618
Electronic ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661821040234

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 4/2021

A Method for Local Anisotropy Recognition in Muon Fluxes Based on Matrix Observations of the URAGAN Hodoscope Using Calculations of Systems of Confidence Intervals and Spatiotemporal Filtering

Detection of Сonvective Сlouds over Indian Region Using Normalized Difference Cloud Index from Satellite Data

An Information Model for Digital Image Segmentation

Optical Flow Estimation with Convolutional Neural Nets

Using Hough-Like Transforms for Extracting Relevant Regularities from Big Applied Data

Bayesian Cellular Automata Fusion Model Based on Dual-Stream Strategy for Video Anomaly Action Detection

Premium Partner