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Microstructure pattern extraction based image retrieval

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Abstract

Computer vision techniques enhanced by the advent of deep learning has become a quintessential part of our day-to-day life. The application of such computer vision techniques in image retrieval can be termed as query based image retrieval process. Conventional methods have limitations such as increased dimensionality, reduced accuracy, high time consumption, and dependence on indexing for retrieval. In order to overcome these limitations, this research work aims to develop a new image retrieval system by developing an image preprocessing mechanism via target prediction technique, which isolates object from the background. Further, a Micro-structure based Pattern Extraction (MPE) technique is implemented to extract the patterns from the preprocessed image, where the diagonal patterns are generated for increasing the accuracy of the retrieval process. Consequently, the Convolutional Neural Network (CNN) is utilized to reduce the dimensionality of the features, and the similarity learning approach is utilized to map the selected features with trained features based on the distance metric. The performance of the proposed system is evaluated by using various measures. Thereby, the efficiency of the proposed technique is ascertained by comparing it with the existing techniques.

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References

  1. Ahmad J, et al (2015) Describing colors, textures and shapes for content based image retrieval-a survey. arXiv preprint arXiv:1502.07041

  2. Brahmaiah Naik J et al (2017) Local vector pattern with global index angles for a content-based image retrieval system. J Assoc Inf Sci Technol 68:2755–2770. https://doi.org/10.1002/asi.23907

    Article  Google Scholar 

  3. Bui T et al (2017) Compact descriptors for sketch-based image retrieval using a triplet loss convolutional neural network. Comput Vis Image Underst 164:27–37. https://doi.org/10.1016/j.cviu.2017.06.007

    Article  Google Scholar 

  4. Datta R et al (2008) Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (Csur) 40:5. https://doi.org/10.1145/1348246.1348248

    Article  Google Scholar 

  5. de Ves E et al (2016) A novel dynamic multi-model relevance feedback procedure for content-based image retrieval. Neurocomputing 208:99–107. https://doi.org/10.1016/j.neucom.2016.02.073

    Article  Google Scholar 

  6. Desai P et al (2016) Performance evaluation of image retrieval systems using shape feature based on wavelet transform. In: 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), pp. 1–5, 10.1109/CCIP.2016.7802876

  7. Fathian M et al (2017) A learning automata framework based on relevance feedback for content-based image retrieval. Int J Mach Learn Cybern:1–16. https://doi.org/10.1007/s13042-017-0656-x

    Article  Google Scholar 

  8. Grycuk R, et al (2016) Content-based image retrieval optimization by differential evolution. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 86–93, 10.1109/CEC.2016.7743782

  9. Guo J-M et al (2015) Content-based image retrieval using error diffusion block truncation coding features. IEEE Transactions on Circuits and Systems for Video Technology 25:466–481. https://doi.org/10.1109/TCSVT.2014.2358011

    Article  Google Scholar 

  10. Khemchandani R, Pal A (2017) Tree based multi-category Laplacian TWSVM for content based image retrieval. Int J Mach Learn Cybern 8:1197–1210. https://doi.org/10.1007/s13042-016-0493-3

    Article  Google Scholar 

  11. Kumar MPH, Modi DNI (2017) A survey on content based image retrieval system using color and texture. 10.5120/802-1139

  12. Kumar A et al (2013) Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. J Digit Imaging 26:1025–1039. https://doi.org/10.1007/s10278-013-9619-2

    Article  Google Scholar 

  13. Kuncheva LI, Faithfull WJ (2014) PCA feature extraction for change detection in multidimensional unlabeled data. IEEE Transactions on Neural Networks and Learning Systems 25:69–80. https://doi.org/10.1109/TNNLS.2013.2248094

    Article  Google Scholar 

  14. Levinskis A (2013) Convolutional neural network feature reduction using wavelet transform. Elektronika ir Elektrotechnika 19:61–64. https://doi.org/10.5755/j01.eee.19.3.3698

    Article  Google Scholar 

  15. Liang R-Z et al (2016) Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2954–2958, 10.1109/ICPR.2016.7900086

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

    Article  Google Scholar 

  17. Liu P et al (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 

  18. Malki Z (2017) Shape and geometric features-based semantic image retrieval using multi-class support vector machine. 10.20944/preprints201702.0077.v1

  19. Meharban M, Priya S (2016) A Review on Image Retrieval Techniques. Bonfring International Journal of Advances in Image Processing 6:7

    Article  Google Scholar 

  20. Moghaddam HA, Ghodratnama S (2017) Toward semantic content-based image retrieval using Dempster–Shafer theory in multi-label classification framework. International Journal of Multimedia Information Retrieval:1–10. https://doi.org/10.1007/s13735-017-0134-y

    Article  Google Scholar 

  21. Moghaddam HA, Ghodratnama S (2017) Toward semantic content-based image retrieval using Dempster–Shafer theory in multi-label classification framework. International Journal of Multimedia Information Retrieval 6:317–326. https://doi.org/10.1007/s13735-017-0134-y

    Article  Google Scholar 

  22. Mohana TK et al (2017) Various Distance Metric Methods for Query Based Image Retrieval. Int J Eng Sci 5818

  23. Mohanan A, Raju S (2017) A Survey on Different Relevance Feedback Techniques in Content Based Image Retrieval. 02

  24. Murala S et al (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21:2874–2886. https://doi.org/10.1109/TIP.2012.2188809

    Article  MathSciNet  MATH  Google Scholar 

  25. Nabil M et al (1996) Picture similarity retrieval using the 2D projection interval representation. IEEE Trans Knowl Data Eng 8:533–539. https://doi.org/10.1109/69.536246

    Article  Google Scholar 

  26. Paulin M et al (2017) Convolutional patch representations for image retrieval: an unsupervised approach. Int J Comput Vis 121:149–168. https://doi.org/10.1007/s11263-016-0924-3

    Article  Google Scholar 

  27. Piras L, Giacinto G (2017) Information fusion in content based image retrieval: A comprehensive overview. Information Fusion 37:50–60. https://doi.org/10.1016/j.inffus.2017.01.003

    Article  Google Scholar 

  28. Shirazi SH et al (2016) Content-Based Image Retrieval Using Texture Color Shape and Region. Int J Adv Comput Sci Appl 7:418–426

    Google Scholar 

  29. Singh VP et al (2017) An efficient image retrieval based on fusion of fast features and query image classification. International Journal of Rough Sets and Data Analysis (IJRSDA) 4:19–37. https://doi.org/10.4018/IJRSDA.2017010102

    Article  Google Scholar 

  30. Tsochatzidis L et al (2017) Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recogn. https://doi.org/10.1016/j.patcog.2017.05.023

    Article  Google Scholar 

  31. Uwimana E, Ruiz ME (2008) Automatic classification of medical images for content based image retrieval systems (CBIR). Proceedings of the Human Factors and Ergonomics Society Annual Meeting:788–792. https://doi.org/10.1177/154193120805201205

    Article  Google Scholar 

  32. Verma M et al (2015) Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing 165:255–269. https://doi.org/10.1016/j.neucom.2015.03.015

    Article  Google Scholar 

  33. Vijendran AS, Kumar SV (2015) A New Content Based Image Retrieval System by HOG of Wavelet Sub Bands. International Journal of Signal Processing, Image Processing and Pattern Recognition 8:297–306. https://doi.org/10.14257/ijsip.2015.8.4.26

    Article  Google Scholar 

  34. Vipparthi SK, Nagar SK (2014) Color directional local quinary patterns for content based indexing and retrieval. Human-Centric Computing and Information Sciences 4:6. https://doi.org/10.1186/s13673-014-0006-x

    Article  Google Scholar 

  35. Wang JZ et al (2001) SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis & Machine Intelligence:947–963. https://doi.org/10.1109/34.955109

    Article  Google Scholar 

  36. Wang L et al (2019) Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2019.2894498

  37. Yadav R et al (2016) Query Based Image Retrieval Using Color Edge Detection Algorithm. Int J Eng Sci 4242

  38. You X et al (2010) A blind watermarking scheme using new nontensor product wavelet filter banks. IEEE Trans Image Process 19(12):3271–3284. https://doi.org/10.1109/TIP.2010.2055570

    Article  MathSciNet  MATH  Google Scholar 

  39. Yu J et al (2013) Feature integration analysis of bag-of-features model for image retrieval. Neurocomputing 120:355–364. https://doi.org/10.1016/j.neucom.2012.08.061

    Article  Google Scholar 

  40. Zheng L et al (2018) SIFT meets CNN: A decade survey of instance retrieval. IEEE Trans Pattern Anal Mach Intell 40:1224–1244. https://doi.org/10.1109/TPAMI.2017.2709749

    Article  Google Scholar 

  41. Zhu L et al (2016) Unsupervised topic hypergraph hashing for efficient mobile image retrieval. IEEE Transactions on Cybernetics 47:3941–3954. https://doi.org/10.1109/TCYB.2016.2591068

    Article  Google Scholar 

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  1. Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh, 517127, India

    S. Priyanka

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Priyanka, S. Microstructure pattern extraction based image retrieval. Multimed Tools Appl 79, 2263–2283 (2020). https://doi.org/10.1007/s11042-019-08113-y

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