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Multiview Objects Recognition Using Deep Learning-Based Wrap-CNN with Voting Scheme

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Abstract

Industrial automation effectively reduces the human effort in various activities of the industry. In many autonomous systems, object recognition plays a vital role. Thus, finding a solution for the accurate recognition of objection for the autonomous system is motivated among researchers. In this sense, various techniques have been designed with the support of classifiers and machine learning techniques. But those techniques lack their performance in the case of Multiview object recognition. It is found that a single classifier or machine learning algorithm is not enough to recognize Multiview objects accurately. In this paper, a Wrap Convolutional Neural Network (Wrap-CNN) with a voting scheme is proposed to solve the Multiview object recognition problem and attain better recognition accuracy. The proposed model consists of three phases such as pre-processing, pre-training CNNs and voting schemes. The pre-processing phase is done to remove the unwanted noise. These pre-trained CNN models are used as feature extractors and classify the images into their respective classes. Here, the Wrap-CNN, nine pre-trained CNN are used in parallels, such as Alex Net, VGGNet, GoogLeNet, Inceptionv3, SqueezeNet, ResNet v2, Xception, MobileNetV2 and ShuffleNet. Finally, the output class from the nine predicted classes is chosen based voting scheme. The system was tested in two scenarios, such as images without rotation and with rotation. The overall accuracy is 99% and 93% for without rotation and with rotation recognition, respectively. Ultimately the system proves the effectiveness for the Multiview object recognition, which can be used for the industrial automation system.

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References

  1. Li H, H Lu, Z Lin, X Shen, and B Price (2015) Lcnn: low-level feature embedded CNN for salient object detection. arXiv preprint

  2. Lowe DG (2001) Local feature view clustering for 3D object recognition. In: proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, 1: I-I). IEEE

  3. Thomas A, Ferrar V, Leibe B, Tuytelaars T, Schiel B. and Gool LV. (2006) Towards multiview object class detection. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06), IEEE, 2: 1589–1596

  4. Pepik B, Stark M, Gehler P, Schiele B (2015) Multiview and 3d deformable part models. IEEE Trans Pattern Anal Mach Intell 37(11):2232–2245

    Article  Google Scholar 

  5. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  6. Simonyan K and Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint

  7. Karthick S, Maniraj S (2019) Different medical image registration techniques: a comparative analysis. Curr Med Imaging 15(10):911–921

    Article  Google Scholar 

  8. Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X and Xiao J (2015) 3d shapenets: a deep representation for volumetric shapes. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1912–1920

  9. Johns E, Aodha OM and Brostow GJ (2015) Becoming the expert-interactive multi-class machine teaching. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2616–2624

  10. Su H, Maji S, Kalogerakis E and Learned-Miller E (2015) Multi-view convolutional neural networks for 3d shape recognition. In: proceedings of the IEEE international conference on computer vision, pp. 945–953

  11. Muneeswaran K, Ganesan L, Arumugam S, Soundar KR (2005) Texture classification with combined rotation and scale invariant wavelet features. Pattern Recogn 38(10):1495–1506

    Article  Google Scholar 

  12. Manipoonchelvi P, Muneeswaran K (2014) Multi region based image retrieval system. Sadhana 39(2):333–344

    Article  Google Scholar 

  13. Manipoonchelvi P, Muneeswaran K (2015) Significant region-based image retrieval. SIViP 9(8):1795–1804

    Article  Google Scholar 

  14. Yang Y, Zhang W, Xie Y (2015) Image automatic annotation via multiview deep representation. J Vis Commun Image Represent 33:368–377

    Article  Google Scholar 

  15. Shi B, Bai S, Zhou Z, Bai X (2015) Deeppano: deep panoramic representation for 3-d shape recognition. IEEE Signal Process Lett 22(12):2339–2343

    Article  Google Scholar 

  16. Khan S, Hayat M, Bennamoun M, Sohel FA, Togneri R (2017) Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans Neural Netw Learn Syst 29(8):3573–3587

    Google Scholar 

  17. Yan Y, Chen M, Shyu ML and Chen SC (2015) Deep learning for imbalanced multimedia data classification. In: 2015 IEEE international symposium on multimedia (ISM), IEEE, pp. 483–488

  18. Huang C, Li Y, Loy CC and Tang X (2016) Learning deep representation for imbalanced classification. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5375–5384

  19. Wu H, Prasad S (2017) Semi-supervised deep learning using pseudo labels for hyperspectral image classification. IEEE Trans Image Process 27(3):1259–1270

    Article  MathSciNet  Google Scholar 

  20. Tang C, Ling Y, Yang X, Jin W, Zheng C (2018) Multiview object detection based on deep learning. Appl Sci 8(9):1423

    Article  Google Scholar 

  21. Rocco I, Arandjelovic R and Sivic J (2017) Convolutional neural network architecture for geometric matching. In: proceedings of the IEEE conference on computer vision and pattern recognition pp. 6148–6157

  22. Wang L, Wang L, Lu H, Zhang P, Ruan X (2018) Salient object detection with recurrent fully convolutional networks. IEEE Trans Pattern Anal Mach Intell 41(7):1734–1746

    Article  Google Scholar 

  23. Shi W, van de Zedde R, Jiang H, Kootstra G (2019) Plant-part segmentation using deep learning and multiview vision. Biosyst Eng 187:81–95

    Article  Google Scholar 

  24. Koohzadi M, Charkari NM, Ghaderi F (2020) Unsupervised representation learning based on the deep multiview ensemble learning. Appl Intell 50(2):562–581

    Article  Google Scholar 

  25. Gao Z, Wang DY, Xue YB, Xu GP, Zhang H, Wang YL (2018) 3D object recognition based on pairwise multiview convolutional neural networks. J Vis Commun Image Represent 56:305–315

    Article  Google Scholar 

  26. Gao Z, Zhang Y, Zhang H, Guan W, Feng D, Chen S (2021) Multi-level view associative convolution network for view-based 3D model retrieval. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2021.3091581

    Article  Google Scholar 

  27. Zhu C, Miao D, Wang Z, Zhou R, Wei L, Zhang X (2020) Global and local multiview multilabel learning. Neurocomputing 371:67–77

    Article  Google Scholar 

  28. Zhu XF, Li XL, Zhang SC (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46(2):450–461

    Article  Google Scholar 

  29. Q.Y. Tan, G.X. Yu, C. Domeniconi, J. Wang, and Z.L. Zhang, (2018) Multi-view weak-label learning based on matrix completion. In: proceedings of the 2018 SIAM international conference on data mining (SIAM 2018), pp. 450–458

  30. Qian BY, Wang X, Ye JP, Davidson I (2015) A reconstruction error based framework for multilabel and multiview learning. IEEE Trans Knowl Data Eng 27(3):594–607

    Article  Google Scholar 

  31. Nie FP, Tian L, Wang R, Li XL (2020) Multiview semi-supervised learning model for image classification. IEEE Trans Knowl Data Eng 32(12):2389–2400

    Article  Google Scholar 

  32. Li H, Lin Z, Shen X, Brandt J. and Hua G (2015) A convolutional neural network cascade for face detection. In: procedings of the IEEE conference on computer vision and pattern recognition, pp. 5325–5334

  33. Ding C, Tao D (2017) Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans Pattern Anal Mach Intell 40(4):1002–1014

    Article  Google Scholar 

  34. Szegedy C, Vanhoucke V, Ioffe S, Shlens J. and Wojna Z (2016) Rethinking the inception architecture for computer vision. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826

  35. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ and Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint

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

  37. Szegedy C, Ioffe S, Vanhoucke V and Alemi A. (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: proceedings of the AAAI conference on artificial intelligence 31(1)

  38. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: proceedings of the EEE conference on computer vision and pattern recognition, pp. 1251–1258

  39. Sandler M, Howard A, Zhu M, Zhmoginov A and Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510–4520

  40. Zhang X, Zhou X, Lin M. and Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6848–6856

  41. Krogh A, Vedelsby J (1995) Validation, and active learning. Adv Neural Inf Process Syst 7(7):231

    Google Scholar 

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

  43. Wang J, Yang Y, Mao J, Huang Z, Huang C and Xu, W. (2016). Cnn-rnn: a unified framework for multilabel image classification. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2285–2294

  44. Wu XZ and Zhou ZH (2017) A unified view of multilabel performance measures. In: international conference on machine learning, pp. 3780–3788, PMLR

  45. Nene SA, Nayar SK and Murase H. (1996) Columbia object image library (coil-100). Link: http://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php

  46. https://www.kaggle.com/balraj98/modelnet40-princeton-3d-object-dataset

  47. Sengan S, Prabhu LAJ, Ramachandran V, Priya V, Ravi L, Subramaniyaswamy V (2020) Images super-resolution by optimal deep AlexNet architecture for medical application: a novel DOCALN. J Intell Fuzzy Syst 39(6):8259–8272

    Article  Google Scholar 

  48. Özyurt F (2020) A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine. Soft Comput 24(11):8163–8172

    Article  Google Scholar 

  49. Jadoon MM, Zhang Q, Haq IU, Butt S, Jadoon A (2017) Three-class mammogram classification based on descriptive CNN features. BioMed Res Int. https://doi.org/10.1155/2017/3640901

    Article  Google Scholar 

  50. Liu Y, B Fan, S Xiang, and C Pan (2019) Relation-shape convolutional neural network for point cloud analysis. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8895–8904

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Authors and Affiliations

  1. Department of CSE, School of Computing (SoC), KL University, Vijayawada Campus, AP, India

    S. S. Aravinth

  2. Computer Science and Engineering, Sona College of Technology, Salem, India

    D. Balamurugan

  3. Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India

    P. Chandra Shaker Reddy

  4. Department of ECE, Manipal University, Jaipur, India

    Ajay Rupani

  5. Department of ECE, Vivekanandha College of Technology for Women, Namakkal, India

    A. Manikandan

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  1. D. Balamurugan
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  2. S. S. Aravinth
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  3. P. Chandra Shaker Reddy
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  4. Ajay Rupani
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  5. A. Manikandan
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Correspondence to D. Balamurugan.

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Balamurugan, D., Aravinth, S.S., Reddy, P.C.S. et al. Multiview Objects Recognition Using Deep Learning-Based Wrap-CNN with Voting Scheme. Neural Process Lett 54, 1495–1521 (2022). https://doi.org/10.1007/s11063-021-10679-4

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