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Deep Convolutional Generalized Classifier Neural Network

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Abstract

Up to date technological implementations of deep convolutional neural networks are at the forefront of many issues, such as autonomous device control, effective image and pattern recognition solutions. Deep neural networks generally utilize a hybrid topology of a feature extractor containing convolutional layers followed by a fully connected classifier network. The characteristic and quality of the produced features differ according to the deep learning structure. In order to get high performance, it is necessary to choose an effective topology. In this study, a novel topology based hybrid structure named as Deep Convolutional Generalized Classifier Neural Network and its learning algoritm are introduced. This novel structure allows the deep learning network to extract features with the desired characteristics. This ensures high performance classification, even for relatively small deep learning networks. This has led to many novelties such as principal feature analysis, better learning ability, one-pass learning for classifier part, new error computation and backpropagation approach for filter weights. Two experiment sets were performed to measure the performance of DC-GCNN. In the first experiment set, DC-GCNN was compared with clasical approach on 10 different datasets. DC-GCNN performed better up to 44.45% for precision, 39.69% for recall and 42.57% for F1-score. In the second experiment set, DC-GCNN’s performance was compared with alternative methods on larger datasets. Proposed structure performed better than alternative deep learning based classifier structures on CIFAR-10 and MNIST datasets with 89.12% and 99.28% accuracy values.

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References

  1. Chandrakumar T, Kathirvel R (2016) Classifying diabetic retinopathy using deep learning architecture. Int J Eng Res Technol (IJERT) 5(6):19–24

    Google Scholar 

  2. Dahl GE (2015) Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing. Ph.D. thesis, Graduate Department of Computer Science University of Toronto

  3. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009, pp 248–255. IEEE

  4. Ferrari V, Jurie F, Schmid C (2010) From images to shape models for object detection. Int J Comput Vision 87(3):284–303

    Article  Google Scholar 

  5. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  6. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708

  7. Krizhevsky A (2009) Learning multiple layers of features from tiny images. Master’s Thesis, Department of Computer Science, University of Toronto

  8. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  9. Lazebnik S, Schmid C, Ponce J (2004) Semi-local affine parts for object recognition. In: British Machine Vision Conference (BMVC’04), pp 779–788. The British Machine Vision Association (BMVA)

  10. Lazebnik S, Schmid C, Ponce J (2005) A maximum entropy framework for part-based texture and object recognition. In: Tenth IEEE international conference on computer vision, 2005. ICCV 2005, vol. 1, pp. 832–838. IEEE

  11. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  12. Nielsen MA (2015) Neural networks and deep learning. Determination Press, Chicago

    Google Scholar 

  13. Nilsback ME, Zisserman A (2006) A visual vocabulary for flower classification. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 2, pp 1447–1454. IEEE

  14. Ozyildirim BM, Avci M (2013) Generalized classifier neural network. Neural Netw 39:18–26

    Article  Google Scholar 

  15. Ozyildirim BM, Avci M (2014) Logarithmic learning for generalized classifier neural network. Neural Netw 60:133–140

    Article  Google Scholar 

  16. Ozyildirim BM, Avci M (2016) One pass learning for generalized classifier neural network. Neural Netw 73:70–76

    Article  Google Scholar 

  17. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang G (2017) Deep learning for health informatics. IEEE J Biomed Health Inf 21(1):4–21

    Article  Google Scholar 

  18. Ren W, Yu Y, Zhang J, Huang K (2014) Learning convolutional nonlinear features for k nearest neighbor image classification. In: 2014 22nd international conference on pattern recognition, pp 4358–4363

  19. Saleh B, Farhadi A, Elgammal A (2013) Object-centric anomaly detection by attribute-based reasoning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 787–794

  20. Sankar M, Batri K, Partvathi R (2016) Earliest diabetic retinopathy classification using deep convolution neural networks. Int J Adv Eng Technol 2(1):460–470

    Google Scholar 

  21. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229

  22. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  23. Söderkvist O (2001) Computer vision classification of leaves from Swedish trees. Master thesis, Linköping University

  24. Sosa-Garcia J, Odone F. Hands on recognition: adding a vision touch to tactile and sound perception for visually impaired users. IEEE Trans Hum Mach Syst (submitted)

  25. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1–9

  26. Tan M, Yu J, Yu Z, Gao F, Rui Y, Tao D (2018) User-click-data-based fine-grained image recognition via weakly supervised metric learning. ACM Trans Multimed Comput Commun Appl (TOMM) 14(3):1–23

    Article  Google Scholar 

  27. Tang Y (2013) Deep learning using linear support vector machines. In: International conference on machine learning 2013: challenges in representation learning workshop (ICML)

  28. Tennakoon R, Mahapatra D, Ro P, Sedai S, Garnavi R (2016) Image quality classification for DR screening using convolutional neural networks. In: Proceedings of the ophthalmic medical image analysis international workshop

  29. Trivedi A. Deep learning part 2: transfer learning and fine-tuning deep convolutional neural networks. http://blog.revolutionanalytics.com/2016/08/deep-learning-part-2.html. Accessed 15 Sept 2019

  30. Wang J, Markert K, Everingham M (2009) Learning models for object recognition from natural language descriptions. In: Proceedings of British machine vision conference

  31. Weber M (2003) 186 images of leaves against different backgrounds. approximate scale normalisation. jpeg format.. Taken in and around Caltech. 896 x 592 jpg format

  32. Winn J, Criminisi A, Minka T (2005) Object categorization by learned universal visual dictionary. In: Tenth IEEE international conference on computer vision, 2005. ICCV 2005, vol 2, pp 1800–1807. IEEE

  33. Yu J, Li J, Yu Z, Huang Q (2019) Multimodal transformer with multi-view visual representation for image captioning. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2019.2947482

    Article  Google Scholar 

  34. Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2019.2932058

    Article  Google Scholar 

  35. Yu J, Tao D, Wang M, Rui Y (2014) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779

    Article  Google Scholar 

  36. Yu J, Zhu C, Zhang J, Huang Q, Tao D (2019) Spatial pyramid-enhanced netvlad with weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst 31(2):661–674. https://doi.org/10.1109/TNNLS.2019.2908982

    Article  Google Scholar 

  37. Yu Z, Yu J, Cui Y, Tao D, Tian Q (2019) Deep modular co-attention networks for visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6281–6290

  38. Yu Z, Yu J, Xiang C, Fan J, Tao D (2018) Beyond bilinear: generalized multimodal factorized high-order pooling for visual question answering. IEEE Trans Neural Netw Learn Syst 29(12):5947–5959

    Article  Google Scholar 

  39. Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27(5):2420–2432

    Article  MathSciNet  Google Scholar 

Download references

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Authors and Affiliations

  1. Computer Engineering Department, Iskenderun Technical University, Hatay, Iskenderun, Turkey

    Mehmet Sarigul

  2. Computer Engineering Department, Cukurova University, Adana, Turkey

    B. Melis Ozyildirim & Mutlu Avci

Authors
  1. Mehmet Sarigul
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  2. B. Melis Ozyildirim
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  3. Mutlu Avci
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Correspondence to Mehmet Sarigul.

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Sarigul, M., Ozyildirim, B.M. & Avci, M. Deep Convolutional Generalized Classifier Neural Network. Neural Process Lett 51, 2839–2854 (2020). https://doi.org/10.1007/s11063-020-10233-8

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  • DOI: https://doi.org/10.1007/s11063-020-10233-8

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