A Genesis of a Meticulous Fusion based Color Descriptor to Analyze the Supremacy between Machine Learning and Deep learning

A Genesis of a Meticulous Fusion based Color Descriptor to Analyze the Supremacy between Machine Learning and Deep learning

Автор: Shikha Bhardwaj, Gitanjali Pandove, Pawan Kumar Dahiya

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 2 vol.12, 2020 года.

Бесплатный доступ

The tremendous advancements in digital technology pertaining to diverse application areas like medical diagnostics, crime detection, defense etc., has led to an exceptional increase in the multimedia image content. This bears an acute requirement of an effectual retrieval system to cope up with the human demands. Therefore, Content-based image retrieval (CBIR) is among the renowned retrieval systems which uses color, texture, shape, edge and other spatial information to extract the basic image features. This paper proposes an efficient and unexcelled hybrid color descriptor which is an amalgamation of color histogram, color moment and color auto-correlogram. In order to determine the predominance between machine learning and deep learning, two machine learning models, Support vector machine (SVM) and Extreme learning machine (ELM) have been tested. Whereas from deep learning category, Cascade forward back propagation neural network (CFBPNN) and Patternnet have been utilized. Finally, from these divergent tested algorithms, CFBPNN attains the highest accuracy and has been selected to enhance the retrieval accuracy of the proposed system. Numerous standard benchmark datasets namely Corel-1K, Corel-5K, Corel-10K, Oxford flower, Coil-100 and Zurich buildings have been tested here and average precision of 97.1%, 90.3%, 87.9%, 98.4%, 98.9% and 82.7% is obtained respectively which is significantly higher than many state-of-the-art related techniques.

Еще

Color moment, Color histogram, Color correlogram, Support vector machine, Extreme learning machine, Cascade forward back propagation neural network, Patternnet neural network

Короткий адрес: https://sciup.org/15017492

IDR: 15017492   |   DOI: 10.5815/ijisa.2020.02.03

Список литературы A Genesis of a Meticulous Fusion based Color Descriptor to Analyze the Supremacy between Machine Learning and Deep learning

  • A. Alzu’bi, A. Amira, and N. Ramzan, “Semantic content-based image retrieval: A comprehensive study”, J. of Vis. Commun. and Image Representation, vol. 32, pp. 20-54, 2015
  • A. K. Naveena, and N. K. Narayanan, “Image Retrieval using combination of Color, Texture and Shape Descriptor”, Proceedings in Next Generation Intelligent Systems (ICNGIS), IEEE, 2016, pp. 1-5.
  • A. R. Kumar, and D. Saravanan, “Content Based Image Retrieval using Color Histogram”, Int. J. of Comp. Science and Information Technology, vol.4, no.2, pp. 242–245, 2013.
  • S.Fadaei, R. Amirfattahi, and M. R. Ahmadzadeh, “New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features”, IET Image Processing, vol. 11, no. 2, pp. 89–98, 2017.
  • V. Naghashi, “Co-occurrence of adjacent sparse local ternary patterns : A feature descriptor for texture and face image retrieval”, Opt. – Int. J. of Light Electron Optics, vol. 157, pp. 877–889, 2018.
  • M. A. Ansari, D. Dixit, Kurchaniya, and P. K. Johari, “An Effective Approach to an Image Retrieval using SVM Classifier”, Int. J. of Computer Sciences and Engg., vol. 5, pp. 64-72, 2018.
  • Y. Mistry, D.T. Ingole, and M.D.Ingole, “Content based image retrieval using hybrid features and various distance metric”, J. of Electrical System of Information Technology, vol. 2016, pp. 1–15, 2017.
  • L. K. Pavithra, and T.S. Sharmila, “An efficient framework for image retrieval using color, texture and edge features”, Comp. and Electrical Engg, vol. 0, pp.1–14, 2017.
  • J. Pradhan, S. Kumar S, and H. Banka, “A hierarchical CBIR framework using adaptive tetrolet transform and novel histograms from color and shape features”, Digit. Signal Process. A Revolution Journal, vol. 82, pp. 258–281, 2018.
  • H. Riddhi, Shaparia, M. Narendra, P. Zankhana, and H. Shah, “Flower Classification using Texture and Color Features”, Kalpa Publications in Computing, vol. 2, pp. 113–118, 2017.
  • A. Huthaifa, M. Saher, and H. Hazem, “A Flower Recognition System Based On Image Processing And Neural Networks”, Int. J. of scientific & technology research, vol. 7, pp. 1-9, 2018.
  • L. Yang, H. Lei, W. Siqi, L. Xianglong, and L Bo, “Efficient Segmentation for Region-based Image Retrieval Using Edge Integrated Minimum Spanning Tree”, Proceedings in 23rd International Conference on Pattern Recognition (ICPR), 2016, 4-8 Dec. ancun, Mexico: pp. 1-6.
  • C. Iakovidou, N. Anagnostopoulos, A. Kapoutsis, et al., “Localizing global descriptors for content based image retrieval”, EURASIP Journal on Advances in Signal Processing, 10.1186/s13634-015-0262-6: pp.1-20, 2015.
  • P. Shreelekha, K. Pritee, and Y. Haruo, “Clustering of hierarchical image database to reduce inter-and intra-semantic gaps in visual space for finding specific image semantics”, J. of Visual Communication and Image Representation, vol. 38, pp. 704-720, 2016.
  • E. Mehdi, E. Aroussi, N. El. Houssif, “Content-Based Image Retrieval Approach Using Color and Texture Applied to Two Databases (Coil-100 and Wang )”, Springer International Publishing, https://doi.org/10.1007/978-3-319-76357-6_5, pp. 49-59, 2018.
  • A. Heba, “Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words”, J. of Computer Sciences, pp.1-10, 2016.
  • L. Shenglan, W. Jun, F. Lin, et al., “Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval”, J. of Latex, arXiv:1609.07615v1 [cs.CV] 24 Sep 2016, pp. 1-14, 2016.
  • S. Sandeep, and R. Rachna, “Content Based Image Retrieval using SVM, NN and KNN Classification”, Int. J. of Advanced Research in Comp. and Comm. Eng., vol. 4, no. 6, pp. 549-552, 2015.
  • P. Tomas, and M. Virginijus, “Comparison of Naïve Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification”, Baltic J. Modern Computing, vol. 5, no.2, pp. 221-232, 2017.
  • K.S. Arun, and V. K. Govindan, “A Hybrid Deep Learning Architecture for Latent Topic-based Image Retrieval”, Data Science Eng, vol. 3, no.2, pp.166–195, 2018.
  • K. Lin, H.F.Yang, and C.S. Chen, “Flower Classification with Few Training Examples via Recalling Visual Patterns from Deep CNN”, Proc. In International Conference on Computer vision, graphics and image process. (CVGIP), 2015, pp. 1-9.
  • L. Weibo, W. Zidong, L. Xiaohui, Z. Nianyin, L. Yurong, and E.A. Fuad, “A survey of deep neural network architectures and their applications”, Neurocomputing, vol. 234, pp. 11-26, 2017.
  • S. Kodituwakku, and S. Selvarajah, “Comparison of color features for image retrieval”, Indian J. of Comp. Science, vol. 1, no. 3, pp. 207–211, 2004.
  • N.M. Sanmukh, and M.L. Tejaswini, “Color Histogram Features for Image Retrieval Systems”, Int. J. of Innovative Research in Science, Engineering and Technology, vol. 3, no. 4, pp.10941-10946, 2014.
  • S.M. Singh, and K. Hemachandran, “Content -Based Image Retrieval using Color Moment and Gabor Texture Feature”, Int. J. of Compter Science Issues, vol. 9, no. 2, pp. 99–309, 2012.
  • V. Vinayak, “CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding”, Int. J. of Computer Applications, vol. 161, no. 9, pp.1–7, 2017.
  • J. Huang, S. K. Kumar, M. Mitra M, et al., “Image indexing using color correlograms”, Proceedings in IEEE Computer Soc. Conf. Computer Vision Pattern Recognition, 1994, 191(3–4): 7, pp.62–768.
  • Fu. Ruigang, Li. Biao, G. Yinghui, and P Wang, “Content-Based Image Retrieval Based on CNN and SVM”, Proceedings in 2nd IEEE International Conference on Computer and Communications, 2016, pp. 1-5.
  • M. Mansourvar, S. Shamshirband, R.G. Raj, R. Gunalan,. and I. Mazinani , “An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines”, PLOS One, doi:10.1371/journal.pone.0138493, pp. 1-14, 2015.
  • S. Liu, H. Wang, J. Wu, and L. Feng, “Incorporate Extreme Learning Machine to content-based image retrieval with relevance feedback”, In Proceedings of 11th World Congress on Intelligent Control and Automation Shenyang, China, 29 june-4 july (IEEE), 2015, pp. 1010-1013.
  • Z. Weixun, N. Shawn, Li. Congmin, S. Zhenfeng, “PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval”, ISPRS Journal of Photogrammetry and Remote Sensing, pp.1-13, 2018.
  • N.A. Omaima, A.A.Tamimi, and M.A. Alia, “Face Recognition System Based on Different Artificial Neural Networks Models and Training Algorithms”, Int. J. of Advances in Computer Science and Applications, vol. 4, pp. 40-47, 2013.
  • N. Arora, A. Ashok, and S. Tiwari, “Efficient Image Retrieval through Hybrid Feature set and Neural Network”, I.J. Image, Graphics and Signal Processing, vol. 1, pp. 44-53, 2019.
  • S. P. Rana, M. Dey, and P. Siarry, “Boosting content based image retrieval performance through integration of parametric & nonparametric approaches”, J. Vis. Commun. Image R., vol. 58, pp. 205-219, 2019.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp