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An efficient content based image retrieval system using BayesNet and K-NN

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Abstract

In the progression of web and multi-media, substantial measure of pictures is created and appropriated, to viably store and offer such vast measure of bulky database is a big issue. In this way, Content Based Image Retrieval (CBIR) techniques are used to retrieve images from the massive database based on the desired information. In this proposed work, we are considering two local image feature extraction methods, namely, SIFT and ORB. Scale Invariant Feature Transform (SIFT) is used for detecting features and feature descriptor of an image. Oriented Fast Rotated and BRIEF (ORB) uses FAST (Features from Accelerated Segment Test) key point detector and binary BRIEF (Binary Robust Independent Elementary Features) descriptor of an image. K-Means clustering algorithm is also used in the present paper for analyzing the data, which generates number of clusters using the descriptor vector. Locality Preserving Projection (LPP) is employed to reduce the length of the feature vector to enhance the performance of image retrieval system. For classification, we have considered two classifiers, namely, BayesNet and K-Nearest Neighbours (K-NN). Wang image dataset has been used for experimentation work. We have accomplished the highest precision rate of 88.9% using proposed CBIR system.

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References

  1. Alhassan AK, Alfaki AA (2017) Color and texture fusion-based method for content-based image retrieval. Proceedings of the International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), 1–6

  2. Anandh A, Mala K, Suganya S (2016) Content based image retrieval system based on semantic information using color, texture and shape features. In the Proceedings of the International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16), 1–8

  3. Fadaei S, Amirfattahi R, Ahmadzadeh MR (2017) A new content-based image retrieval system based on optimized integration of DCD, wavelet and Curvelet features. IET Image Process 11(2):89–98

    Article  Google Scholar 

  4. Giveki D, Soltanshahi MA, Montazer GA (2017) A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern. Optik - Int J Light Electron Opt 131:242–254

    Article  Google Scholar 

  5. Guo JM, Prasetyo H (2015) Content-based image retrieval using features extracted from halftoning-based block truncation coding. IEEE Trans Image Process 24(3):1010–1024

    Article  MathSciNet  Google Scholar 

  6. Kumar TGS, Nagarajan V (2016) Combining LBP and Contourlet features for image retrieval. In the Proceedings of the International Conference on Communication and Signal Processing (ICCSP), 1193–1196

  7. Liu P, Guo JM, Chamnongthai K, Prasetyo H (2017) Fusion of color histogram and lbp-based features for texture image retrieval and classification. Inf Sci 390:95–111

    Article  Google Scholar 

  8. Namdeo HD, Jadhav PD (2015) Content based image retrieval using color and texture. Int J Electron Commun Instrum Eng 5(3):81–88

    Google Scholar 

  9. Nistér D, Stewénius H (2006) Scalable recognition with a vocabulary tree. Proceedings of the 2006 I.E. computer society conference on computer vision and pattern recognition 2:2161–2168

  10. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  11. Singha M, Hemachandran K (2012) Content based image retrieval using color and texture. Signal Image Process Int J (SIPIJ) 3(1):39–57

    Article  Google Scholar 

  12. Soltanshahi MA, Montazer GA, Giveki D (2015) Content based image retrieval system using clustered scale invariant feature transforms. Optik - Int J Light Electron Opt 126(18):1695–1699

    Article  Google Scholar 

  13. Srivastava P, Khare A (2016) Content-based image retrieval using scale invariant feature transform and moments. In the Proceedings of the section international conference on electrical, computer and electronics engineering, 162–166

  14. Vinay A, Kumar CA, Shenoy GR, Murthy KNB, Natarajan S (2015) ORB-PCA based feature extraction technique for face recognition. Procedia Comput Sci 58:614–621

    Article  Google Scholar 

  15. Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circ Syst Video Technol 24(12):2077–2089

    Article  Google Scholar 

  16. Yan C, Zhang Y, Dai F, Wang X, Li L, Dai Q (2014) Parallel deblocking filter for HEVC on many-core processor. Electron Lett 50(5):367–368

    Article  Google Scholar 

  17. Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2017) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 99:1–12

    Google Scholar 

  18. Yan C, Xie H, Liu S, Yin J, Zhang Y, Dai Q (2017) Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans Intell Transp Syst 99:1–10

    Google Scholar 

  19. Zhuo L, Cheng B, Zhang J (2014) A comparative study of dimensionality reduction methods for large-scale image retrieval. Neurocomputing 141:202–210

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Applications, GZS Campus College of Engineering & Technology (Maharaja Ranjit Singh Punjab Technical University), Bathinda, Punjab, 151001, India

    Munish Kumar

  2. Department of Computer Science & Engineering, GZS Campus College of Engineering & Technology (Maharaja Ranjit Singh Punjab Technical University), Bathinda, Punjab, 151001, India

    Payal Chhabra & Naresh Kumar Garg

Authors
  1. Munish Kumar
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  2. Payal Chhabra
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  3. Naresh Kumar Garg
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Correspondence to Munish Kumar.

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Kumar, M., Chhabra, P. & Garg, N.K. An efficient content based image retrieval system using BayesNet and K-NN. Multimed Tools Appl 77, 21557–21570 (2018). https://doi.org/10.1007/s11042-017-5587-8

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  • DOI: https://doi.org/10.1007/s11042-017-5587-8

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