Skip to main content
SpringerLink
Log in

Efficient hybrid multi-level matching with diverse set of features for image retrieval

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Content-based image retrieval has become popular in the retrieval of images from large image database using reduced human intervention. Researchers are still in need to develop effective systems for dealing many of the wide-scope scientific and medical applications. Past research works have faced a problem on differentiating different images by means of using the single features alone. In this paper, a multi-level matching scheme is introduced for retrieval of image based on a hybrid feature similarity integrating local and global features. Both global- and local-level features included in multi-level scheme are used for image representation. From an image, the color information is extracted globally using a new color, edge directivity descriptor and color-based features. Further, the interest of points from each image is detected using local descriptors called local binary pattern and speeded-up robust features. Using two image databases, the improved retrieval accuracy obtained with the combination of global and local features is analyzed. Experimental outcomes have revealed the effectiveness of proposed system on achieving 91% and 92% precision rates over two datasets compared to other existing methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Abbreviations

CBIR:

Content-based image retrieval

CRF:

Color-related feature

BoW:

Bag-of-words

IR:

Image retrieval

VOC:

Visual object classes

CCM:

Color co-occurrence matrix

SVM:

Support vector machine

DWT:

Discrete wavelet transform

PNN:

Probabilistic neural network

CRF:

Color-related feature

MLM:

Multi-level matching

K-NN:

K-nearest neighbor

FRAR:

Full range autoregressive

RBFNN:

Radial basis function neural network

EOAC:

Edge orientation auto-correlogram

SOM:

Self-organizing map

CHF:

Color histogram feature

CEED:

Color and edge directivity descriptor

TSK:

Takagi–Sugeno–Kang

LBP:

Local binary pattern

SURF:

Speeded-up robust features

NMS:

Non-maximum suppression

SIFT:

Scale-invariant feature transform

SM:

Similarity matching

ED:

Euclidian distance

AUC:

Area under the precision-recall curve

MD:

Manhattan distance

CD:

Canberra distance

MLM:

Multi-level matching

References

  • Al-Janabi S, Alkaim AF (2019) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput. https://doi.org/10.1007/s00500-019-03972-x

    Article  Google Scholar 

  • Al-Janabi S, Mahdi MA (2019) Evaluation prediction techniques to achievement an optimal biomedical analysis. Int J Grid Util Comput 10(5):512–527

    Google Scholar 

  • Amato G, Carrara F, Falchi F, Gennaro C, Vadicamo L (2019) Large-scale instance-level image retrieval. Inf Process Manag, In press, corrected proof, Available online 29 Aug 2019, Article 102100

  • Anandh A,Mala K,Suganya S (2016) Content based image retrieval system based on semantic information using color, texture and shape features. In: 2016 International conference on computing technologies and intelligent data engineering (ICCTIDE’16), pp 1–8. IEEE

  • Argyriou V, Tzimiropoulos G (2017) Frequency domain subpixel registration using HOG phase correlation. Comput Vis Image Underst 155:70–82

    Google Scholar 

  • Bagri N, Johari PK (2015) A comparative study on feature extraction using texture and shape for content based image retrieval. Int J Adv Sci Technol 80(4):41–52

    Google Scholar 

  • Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

    Google Scholar 

  • Dubey SR, Singh SK, Singh RK (2014) Rotation and illumination invariant interleaved intensity order-based local descriptor. IEEE Trans Image Process 23(12):5323–5333

    MathSciNet  MATH  Google Scholar 

  • El Alami ME (2011) A novel image retrieval model based on the most relevant features. Knowl-Based Syst 24(1):23–32

    Google Scholar 

  • El Alami ME (2014) A new matching strategy for content based image retrieval system. Appl Soft Comput 14:407–418

    Google Scholar 

  • Gladis KPA (2019) Integration of global and local features based on hybrid similarity matching scheme for medical image retrieval system. Int J Biomed Eng Technol 31(3):292–314

    Google Scholar 

  • Guo J-M, Prasetyo H, Chen J-H (2015) Content-based image retrieval using error diffusion block truncation coding features. IEEE Trans Circuits Syst Video Technol 25(3):466–481

    Google Scholar 

  • Hongpeng Z (2019) Massive-scale image retrieval based on deep visual feature representation. J Vis Commun Image Represent, In press, journal pre-proof, Available online 6 Dec 2019, Article 102738

  • Iakovidou C, Anagnostopoulos N, Kapoutsis A, Boutalis Y, Lux M, Chatzichristofis SA (2015) Localizing global descriptors for content-based image retrieval. EURASIP J Adv Signal Process 2015(1):80

    Google Scholar 

  • Korytkowski M, Rutkowski L, Scherer R (2016) Fast image classification by boosting fuzzy classifiers. Inf Sci 327:175–182

    MathSciNet  Google Scholar 

  • Kumar A, Kim J, Wena L, Fulham M, Feng D (2014) A graph-based approach for the retrieval of multi-modality medical images. Med Image Anal 18:330–342

    Google Scholar 

  • Lai H, Yan P, Shu X, Wei Y, Yan S (2016) Instance-aware hashing for multi-label image retrieval. IEEE Trans Image Process 25(6):2469–2479

    MathSciNet  MATH  Google Scholar 

  • Li B, Lu Y, Li C, Godil A, Schreck T, Aono M, Burtscher M, Chen Q, Chowdhury NK, Fang B, Fu H (2015) A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries. Comput Vis Image Underst 131:1–27

    Google Scholar 

  • Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  • Montazer GA, Giveki D (2015) Content based image retrieval system using clustered scale invariant feature transforms. Optik 126(18):1695–1699

    Google Scholar 

  • Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    MATH  Google Scholar 

  • Peng X, Zhang X, Li Y, Liu B (2019) Research on image feature extraction and retrieval algorithms based on convolutional neural network. J Vis Commun Image Represent, In press, journal pre-proof, Available online 11 Nov 2019, Article 102705

  • Ramesh B, Xiang C, Lee TH (2015) Shape classification using invariant features and contextual information in the bag-of-words model. Pattern Recognit 48(3):894–906

    Google Scholar 

  • Schnorrenberg F, Pattichis CS, Schizas CN, Kyriacou K (2000) Content-based retrieval of breast cancer biopsy slides. Technol Health Care 8(5):291–297

    Google Scholar 

  • Shrivastava N, Tyagi V (2015) An efficient technique for retrieval of color images in large databases. Comput Electr Eng 46:314–327

    Google Scholar 

  • Somasundaran BV, Soundararajan R, Biswas S (2020) Robust image retrieval by cascading a deep quality assessment network. Signal Process: Image Commun 80:115652

    Google Scholar 

  • Srinivas M, Naidu rr, Sastry CS, Mohan CK (2015) Content based medical image retrieval using dictionary learning. J Sci Direct, pp 1–19

  • Sujatha K, Shalini Punithavathani D (2018) Optimized ensemble decision-based multi-focus imagefusion using binary genetic Grey-Wolf optimizer in camera sensor networks. Multimed Tools Appl 77(2):1735–1759

    Google Scholar 

  • Sundararaj V (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325–345

    Google Scholar 

  • Sundararaj V, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288

    Google Scholar 

  • Tesfaye AL, Pelillo M (2019) Multi-feature fusion for image retrieval using constrained dominant sets. Image Vis Comput, In press, journal pre-proof, Available online 12 Dec 2019, Article 103862

  • Tian X, Zhou X, Ng WWY, Li J, Wang H (2019) Bootstrap dual complementary hashing with semi-supervised re-ranking for image retrieval. Neurocomputing, In press, corrected proof, Available online 31 Oct 2019

  • Vinu S (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126

    Google Scholar 

  • Vinu S (2019) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel Pers Commun 104(1):173–197

    Google Scholar 

  • Vipparthi SK, Nagar SK (2014) Color directional local quinary patterns for content based indexing and retrieval. Hum-Centric Comput Inf Sci 4(1):6

    Google Scholar 

  • Wang J, Li J, Wiederhold G (2001) Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963

    Google Scholar 

  • Wang XY, Liang LL, Li YW, Yang HY (2017) Image retrieval based on exponent moments descriptor and localized angular phase histogram. Multimedia Tools Appl 76(6):7633–7659

    Google Scholar 

  • Xiao Y,Wu J,Yuan J (2014) mCENTRIST: a multi-channel feature generation mechanism for scene categorization. IEEE Trans Image Process 23(2)

  • Zeng X, Zhang Y, Wang X, Chen K, Li D, Yang W (2019) Fine-grained image retrieval via piecewise cross entropy loss. Image Vis Comput, In press, corrected proof, Available online 1 Nov 2019, Article 103820

  • Zheng Y, Jiang Z, Zhang H, Xie F, Ma Y, Shi H, Zhao Y (2018) Histopathological whole slide image analysis using context-based CBIR. IEEE Trans Med Imaging 37(7):1641–1652

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Kongu Engineering College, Perundurai, Erode, India

    V. Geetha, V. Anbumani & S. Sasikala

  2. Department of ECE, P.A. College of Engineering and Technology, Coimbatore, India

    L. Murali

Authors
  1. V. Geetha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Anbumani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Sasikala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L. Murali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Geetha.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by V. Loia.

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Geetha, V., Anbumani, V., Sasikala, S. et al. Efficient hybrid multi-level matching with diverse set of features for image retrieval. Soft Comput 24, 12267–12288 (2020). https://doi.org/10.1007/s00500-020-04671-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04671-8

Keywords

Search

Navigation