Feature Extraction Framework for CBIR Systems based on Cyclic Transform Analysis & Spatial Information
Subject Areas : Electronics Engineering
Shahin Shafei
1
,
Hamid Vahdati
2
,
Tohid Sedghi
3
,
Asghar Charmin
4
1 - Department of Electrical Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran
2 - 1Department of Electrical Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran
3 - Microwave and Antenna Research center, Urmia Branch, Islamic Azad University, Urmia, Iran
Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
4 - Department of Electrical Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran
Keywords: CBIR, Cyclic Transform Analysis, Retrieval, pattern recognition,
Abstract :
A novel framework of feature generation for Content based image retrieval (CBIR) is proposed. This system is realized on Cyclic transform Analysis (CTA). It introduces statistical descriptors in the signals frequency domain. Then the CT of data is computed by Semi supervised algorithm (SSA) which is a simple & efficient algorithm. Presented Features are Norm-1 & energy CTA extracted from different sections of bi-frequency plane. This layout illustrate good characteristic in database. In addition, this manuscript illustrates a novel framework for generating textural and spatial information, and higher retrieval percentages. The textural features extracted with proposed CTA utilizing first & second moments among the image tiles is so effective in data processing. Spatial information is extracted utilizing decent field matrix (DFM). After that, moments are computed from DFM to get spatial features. The composition of the textural features and conjunction with the spatial information leads to a fantastic features matrix for retrieval. The experimental results on database guaranty the method efficiency on all classes of database with more than 10000 image. For measuring the distance of features a simple matching system based on Minkowski & Canberra distances is introduced. The results are compared with previous scholars and retrieval percentage is increased more than 10% in comparison with previous systems.
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[5] M.h.Hajigholam, A.A.Raie, and K. Faez, “Using Sparse Representation Classifier (SRC) to Calculate Dynamic Coefficients for Multitask Joint Spatial Pyramid, ” Matchingranian Journal of Science and Technology Transactions of Electrical Engineering, vol. 45, pp. 295–307, 2021,doi: 10.1007/s40998-020-00351-3.
[6] H. C. Shin , “Deep convolutional neural networks for computer-aided detection CNN architectures, dataset characteristics and transfer learning, ”IEEE Trans. Med. Imag., vol. 35, no. 5, pp. 1285-1298, May. 2019, doi:10.1109/tmi.2016.2528162.
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[8] F.Rahdari, E.Rashedi, M.Eftekhari, “ A Multimodal Emotion Recognition System Using Facial Landmark Analysis , ” Iranian Journal of Science and Technology Transactions of Electrical Engineering, ” vol. 43, pp. 171–189, Sep. 2019, doi:10.3233/AIC-20063.1.
[9] G Hassan, KM Hosny, RM Farouk, AM Alzohairy, “An efficient retrieval system for biomedical images based on Radial Associated Laguerre Moments” IEEE Access vol. 8, pp. 175669- 175687 , Sep. 2020,doi: 10.1109/ACCESS.2020.3026452.
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[11] KM Hosny, RM Farouk, AM Alzohairy G Hassan, “Efficient Quaternion Moments for Representation and Retrieval of Biomedical Color Images”, Biomedical Engineering: Applications, Basis and Communications vol. 32, pp. 1-16, Oct. 2020, doi:10.4015/S1016237220500398.
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[1] T. Sedghi, “A Fast and Effective Model for Cyclic Analysis and Its Application in Classification, ” Arab Journal Sci.Eng, vol.3, pp.927–935, Sep. 2013, doi:10.1007/s13369-012-0364-5.
[2] J. Li, J. Wang, J. Z. Wiederhold, “integrated region matching for image retrieval in proceedings,” the 8th ACM International Conference on Multimedia, pp. 147- 156, Oct .2015, doi:10.1145/354384.354452.
[3] F .Baig, Z .Mehmood, M .Rashid, M. Arshad. Javid, A .Rehman, T. Saba, and A. Adnan, “Boosting the Performance of the BoVW Model Using SURF–Co HOG-Based Sparse Features with Relevance Feedback for CBIR, ” Iranian Journal of Science and Technology Transactions of Electrical Engineering, vol. 44, p.p 99–118, Aug 2020, doi:10.1186/s13640-020-00516-4.
[4] P. Wu, S. C. H. Hoi, P. Zhao, C. Miao, and Z.-Y. Liu, “Online multi-modal distance metric learning with application to image retrieval, ” IEEE Trans. Knowl. Data Eng., vol. 28, no. 2, pp. 454-467,Feb. 2016, doi: 10.1145/2502081.2502112.
[5] M.h.Hajigholam, A.A.Raie, and K. Faez, “Using Sparse Representation Classifier (SRC) to Calculate Dynamic Coefficients for Multitask Joint Spatial Pyramid, ” Matchingranian Journal of Science and Technology Transactions of Electrical Engineering, vol. 45, pp. 295–307, 2021,doi: 10.1007/s40998-020-00351-3.
[6] H. C. Shin , “Deep convolutional neural networks for computer-aided detection CNN architectures, dataset characteristics and transfer learning, ”IEEE Trans. Med. Imag., vol. 35, no. 5, pp. 1285-1298, May. 2019, doi:10.1109/tmi.2016.2528162.
[7] S.Shafei, H.Vahdati, T. Sedghi, and A. Charmin, “Novel high level retrieval system based on mathematic algorithm & technique for MRI medical imaging and classification, ”Journal of Instrumentation.,vol. 16, no. 7, pp. 1-14, July. 2021,doi:10.1088/1748-0221/16/07/P07055.
[8] F.Rahdari, E.Rashedi, M.Eftekhari, “ A Multimodal Emotion Recognition System Using Facial Landmark Analysis , ” Iranian Journal of Science and Technology Transactions of Electrical Engineering, ” vol. 43, pp. 171–189, Sep. 2019, doi:10.3233/AIC-20063.1.
[9] G Hassan, KM Hosny, RM Farouk, AM Alzohairy, “An efficient retrieval system for biomedical images based on Radial Associated Laguerre Moments” IEEE Access vol. 8, pp. 175669- 175687 , Sep. 2020,doi: 10.1109/ACCESS.2020.3026452.
[10] Nazgol Hor, Shervan Fekri-Ershad, “Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information”, International Journal of Computer Science Engineering, Vol. 8 No.06, pp. 246-254, Dec 2019, doi:10.48550/arXiv.1912.12978.
[11] KM Hosny, RM Farouk, AM Alzohairy G Hassan, “Efficient Quaternion Moments for Representation and Retrieval of Biomedical Color Images”, Biomedical Engineering: Applications, Basis and Communications vol. 32, pp. 1-16, Oct. 2020, doi:10.4015/S1016237220500398.