Retail Product Classification on Distinct Distribution of Training and Evaluation Data
Retail product classification can be beneficial in the world of commerce, take for example helping vision-disabled parties in their shopping or evaluating product placement strategy. However, the available datasets for retail product classification are few and some have very distinct distribution of...
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| Published in | Pattern recognition and image analysis Vol. 32; no. 1; pp. 142 - 152 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Moscow
Pleiades Publishing
01.03.2022
Springer Nature B.V |
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| Abstract | Retail product classification can be beneficial in the world of commerce, take for example helping vision-disabled parties in their shopping or evaluating product placement strategy. However, the available datasets for retail product classification are few and some have very distinct distribution of training and evaluation data, thus providing a huge challenge on its own. In addition, there are only few researches on this subject which can still be improved on. This paper attempts to improve previous approaches for retail product classification on very distinct training and evaluation data distribution by utilizing convolutional neural network (CNN) models inspired by well-performing CNN models in general image classification task, which later can be fine-tuned for other computer vision tasks, namely, object detection. The results show that VGG-16 performs at 66.9167% accuracy and a new modified VGG-16 model named VGG-16-D attains 66.83% accuracy with 85% fewer parameters than VGG-16, outperforming most existing approaches considering several comparison baselines. |
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| AbstractList | Retail product classification can be beneficial in the world of commerce, take for example helping vision-disabled parties in their shopping or evaluating product placement strategy. However, the available datasets for retail product classification are few and some have very distinct distribution of training and evaluation data, thus providing a huge challenge on its own. In addition, there are only few researches on this subject which can still be improved on. This paper attempts to improve previous approaches for retail product classification on very distinct training and evaluation data distribution by utilizing convolutional neural network (CNN) models inspired by well-performing CNN models in general image classification task, which later can be fine-tuned for other computer vision tasks, namely, object detection. The results show that VGG-16 performs at 66.9167% accuracy and a new modified VGG-16 model named VGG-16-D attains 66.83% accuracy with 85% fewer parameters than VGG-16, outperforming most existing approaches considering several comparison baselines. |
| Author | Jonathan Kusuma, Gede Putra |
| Author_xml | – sequence: 1 surname: Jonathan fullname: Jonathan email: jonathan016@binus.ac.id organization: Computer Science Department, BINUS Graduate Program–Master of Computer Science, Bina Nusantara University – sequence: 2 givenname: Gede Putra surname: Kusuma fullname: Kusuma, Gede Putra email: inegara@binus.edu organization: Computer Science Department, BINUS Graduate Program–Master of Computer Science, Bina Nusantara University |
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| Copyright | Pleiades Publishing, Ltd. 2022. ISSN 1054-6618, Pattern Recognition and Image Analysis, 2022, Vol. 32, No. 1, pp. 142–152. © Pleiades Publishing, Ltd., 2022. |
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| Keywords | deep learning image classification retail product classification dilated convolution convolutional neural network |
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| References | J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu, Hawaii,2017 (IEEE, 2017), vol. 1, pp. 6517–6525. https://doi.org/10.1109/CVPR.2017.690 J. Redmon, Darknet: Open source neural networks in C (2013). https://pjreddie.com/darknet G. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature.” arXiv:1207.0580 [cs.NE] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement.” arXiv:1804.02767 [cs.CV] LiQ.PengX.CaoL.DuW.XingH.QiaoY.Product image recognition with guidance learning and noisy supervisionComput. Vision Image Understanding201919610296310.1016/j.cviu.2020.102963 J. Deng, W. Dong, R. Socher, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, 2009 (IEEE, 2019), pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848 Y. Lecun, C. Cortes, and C. Burges, “MNIST handwritten digit database,” ATT Labs. http://yann.lecun.com/exdb/mnist S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu, Hawaii,2017 (IEEE, 2017), pp. 5987–5995. https://doi.org/10.1109/CVPR.2017.634 LiuW.AnguelovD.ErhanD.SzegedyC.ReedS.FuC.-Y.BergA. C. “SSD: Single shot multiBox detector,” in Computer Vision–ECCV 20162016ChamSpringer10.1007/978-3-319-46448-0_2 SantraB.PaulA.MukherjeeD. P.Deterministic Dropout for Deep Neural Networks Using Composite Random ForestPattern Recognit. Lett.202013120521210.1016/j.patrec.2019.12.023 M. Merler, C. Galleguillos, and S. Belongie, “Recognizing groceries in situ using in vitro training data,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Minneapolis,2007 (IEEE, 2007), pp. 1–8. https://doi.org/10.1109/CVPR.2007.383486 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas,2015 (IEEE, 2015), pp. 779–788. https://doi.org/10.1109/CVPR.2016.91 A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” NIPS’12: Proc. 25th Int. Conf. on Neural Information Processing Systems, Lake Tahoe, Nev.,2012, Ed. by F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Curran Associates, Red Hook, N.Y., 2012), Vol. 1, pp. 1097–1105. F. Yu, V. Koltun, and T. Funkhouser, “Dilated residual networks,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii,2017 (IEEE, 2017), pp. 636–644. https://doi.org/10.1109/CVPR.2017.75 K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Int. Conf. on Learning Representations,2015. arXiv:1409.1556 [cs.CV] D. G. Lowe, “Object recognition from local scale-invariant features,” in Proc. 7th Int. Conf. on Computer Vision, Corfu,1999 (IEEE, 1999), Vol. 2, pp. 1150–1157. https://doi.org/10.1109/ICCV.1999.790410 A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier Nonlinearities Improve Neural Network Acoustic Models,” in Proceedings of the 30th Int. Conf. on Machine Learning, Atlanta,2013. RajagopalR.Comparative analysis of COVID-19 X‑ray images classification using convolutional neural network, transfer learning, and machine learning classifiers using deep featuresPattern Recognit. Image Anal.20213131332210.1134/S1054661821020140 M. Lin, Q. Chen, and S. Yan, “Network in network.” arXiv:1312.4400v3 [cs.NE] A. C. Wilson, R. Roelofs, M. Stern, N. Srebro, and B. Recht, “The Marginal Value of Adaptive Gradient Methods in Machine Learning,” in 31st Conf. on Neural Information Processing Systems (NIPS 2017), Long Beach, Calif.,2017, Ed. by U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus (Curran Associates, Red Hook, N. Y., 2017), pp. 4151–4161. GeorgeM.FloerkemeierC. “Recognizing products: A per-exemplar multi-label image classification approach,” in Computer Vision–ECCV 20142014ChamSpringer10.1007/978-3-319-10605-2_29 LeCunY.BottouL.BengioY.HaffnerP.Gradient-based learning applied to document recognitionProc. IEEE1998862278223410.1109/5.726791 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas,2016 (IEEE, 2016), pp. 770–778. https://doi.org/10.1109/CVPR.2016.90 SrivastavaN.HintonG.KrizhevskyA.SutskeverI.SalakhutdinovR.Dropout: A simple way to prevent neural networks from overfittingJ. Mach. Learning Res.2014151929195832315921318.68153 LoweD. G.Distinctive image features from scale-invariant key pointsInt. J. Comput. Vision2004609111010.1023/B:VISI.0000029664.99615.94 RussakovskyO.DengJ.SuH.KrauseJ.SatheeshS.MaS.HuangZ.KarpathyA.KhoslaA.BernsteinM.BergA. C.Fei-FeiL.ImageNet large scale visual recognition challengeInt. J. Comput. Vision2015115211252342248210.1007/s11263-015-0816-y SrivastavaM. M. “Bag of tricks for retail product image classification,” Image Analysis and Recognition. ICIAR 20202020ChamSpringer10.1007/978-3-030-50347-5_8 6220_CR1 6220_CR3 cr-split#-6220_CR25.1 cr-split#-6220_CR25.2 B. Santra (6220_CR21) 2020; 131 W. Liu (6220_CR10) 2016 6220_CR11 6220_CR13 6220_CR14 6220_CR19 6220_CR16 6220_CR17 6220_CR18 M. George (6220_CR2) 2014 D. G. Lowe (6220_CR12) 2004; 60 R. Rajagopal (6220_CR15) 2021; 31 M. M. Srivastava (6220_CR23) 2020 Q. Li (6220_CR8) 2019; 196 N. Srivastava (6220_CR24) 2014; 15 6220_CR22 O. Russakovsky (6220_CR20) 2015; 115 6220_CR5 6220_CR4 6220_CR7 6220_CR9 6220_CR26 6220_CR27 Y. LeCun (6220_CR6) 1998; 86 |
| References_xml | – reference: SrivastavaM. M. “Bag of tricks for retail product image classification,” Image Analysis and Recognition. ICIAR 20202020ChamSpringer10.1007/978-3-030-50347-5_8 – reference: RussakovskyO.DengJ.SuH.KrauseJ.SatheeshS.MaS.HuangZ.KarpathyA.KhoslaA.BernsteinM.BergA. C.Fei-FeiL.ImageNet large scale visual recognition challengeInt. J. Comput. Vision2015115211252342248210.1007/s11263-015-0816-y – reference: LoweD. G.Distinctive image features from scale-invariant key pointsInt. J. Comput. Vision2004609111010.1023/B:VISI.0000029664.99615.94 – reference: Y. Lecun, C. Cortes, and C. Burges, “MNIST handwritten digit database,” ATT Labs. http://yann.lecun.com/exdb/mnist – reference: M. Lin, Q. Chen, and S. Yan, “Network in network.” arXiv:1312.4400v3 [cs.NE] – reference: J. Redmon, Darknet: Open source neural networks in C (2013). https://pjreddie.com/darknet/ – reference: A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier Nonlinearities Improve Neural Network Acoustic Models,” in Proceedings of the 30th Int. Conf. on Machine Learning, Atlanta,2013. – reference: LiuW.AnguelovD.ErhanD.SzegedyC.ReedS.FuC.-Y.BergA. C. “SSD: Single shot multiBox detector,” in Computer Vision–ECCV 20162016ChamSpringer10.1007/978-3-319-46448-0_2 – reference: LeCunY.BottouL.BengioY.HaffnerP.Gradient-based learning applied to document recognitionProc. IEEE1998862278223410.1109/5.726791 – reference: RajagopalR.Comparative analysis of COVID-19 X‑ray images classification using convolutional neural network, transfer learning, and machine learning classifiers using deep featuresPattern Recognit. Image Anal.20213131332210.1134/S1054661821020140 – reference: GeorgeM.FloerkemeierC. “Recognizing products: A per-exemplar multi-label image classification approach,” in Computer Vision–ECCV 20142014ChamSpringer10.1007/978-3-319-10605-2_29 – reference: J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement.” arXiv:1804.02767 [cs.CV] – reference: K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas,2016 (IEEE, 2016), pp. 770–778. https://doi.org/10.1109/CVPR.2016.90 – reference: LiQ.PengX.CaoL.DuW.XingH.QiaoY.Product image recognition with guidance learning and noisy supervisionComput. Vision Image Understanding201919610296310.1016/j.cviu.2020.102963 – reference: G. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature.” arXiv:1207.0580 [cs.NE] – reference: S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu, Hawaii,2017 (IEEE, 2017), pp. 5987–5995. https://doi.org/10.1109/CVPR.2017.634 – reference: K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Int. Conf. on Learning Representations,2015. arXiv:1409.1556 [cs.CV] – reference: SrivastavaN.HintonG.KrizhevskyA.SutskeverI.SalakhutdinovR.Dropout: A simple way to prevent neural networks from overfittingJ. Mach. Learning Res.2014151929195832315921318.68153 – reference: J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu, Hawaii,2017 (IEEE, 2017), vol. 1, pp. 6517–6525. https://doi.org/10.1109/CVPR.2017.690 – reference: M. Merler, C. Galleguillos, and S. Belongie, “Recognizing groceries in situ using in vitro training data,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Minneapolis,2007 (IEEE, 2007), pp. 1–8. https://doi.org/10.1109/CVPR.2007.383486 – reference: F. Yu, V. Koltun, and T. Funkhouser, “Dilated residual networks,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii,2017 (IEEE, 2017), pp. 636–644. https://doi.org/10.1109/CVPR.2017.75 – reference: D. G. Lowe, “Object recognition from local scale-invariant features,” in Proc. 7th Int. Conf. on Computer Vision, Corfu,1999 (IEEE, 1999), Vol. 2, pp. 1150–1157. https://doi.org/10.1109/ICCV.1999.790410 – reference: SantraB.PaulA.MukherjeeD. P.Deterministic Dropout for Deep Neural Networks Using Composite Random ForestPattern Recognit. Lett.202013120521210.1016/j.patrec.2019.12.023 – reference: J. Deng, W. Dong, R. Socher, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, 2009 (IEEE, 2019), pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848 – reference: A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” NIPS’12: Proc. 25th Int. Conf. on Neural Information Processing Systems, Lake Tahoe, Nev.,2012, Ed. by F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Curran Associates, Red Hook, N.Y., 2012), Vol. 1, pp. 1097–1105. – reference: J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas,2015 (IEEE, 2015), pp. 779–788. https://doi.org/10.1109/CVPR.2016.91 – reference: A. C. Wilson, R. Roelofs, M. Stern, N. Srebro, and B. Recht, “The Marginal Value of Adaptive Gradient Methods in Machine Learning,” in 31st Conf. on Neural Information Processing Systems (NIPS 2017), Long Beach, Calif.,2017, Ed. by U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus (Curran Associates, Red Hook, N. Y., 2017), pp. 4151–4161. – volume: 15 start-page: 1929 year: 2014 ident: 6220_CR24 publication-title: J. Mach. Learning Res. – volume-title: “SSD: Single shot multiBox detector,” in Computer Vision–ECCV 2016 year: 2016 ident: 6220_CR10 doi: 10.1007/978-3-319-46448-0_2 – ident: 6220_CR16 – ident: 6220_CR11 doi: 10.1109/ICCV.1999.790410 – ident: #cr-split#-6220_CR25.2 – volume: 131 start-page: 205 year: 2020 ident: 6220_CR21 publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2019.12.023 – ident: 6220_CR3 doi: 10.1109/CVPR.2016.90 – ident: 6220_CR27 doi: 10.1109/CVPR.2017.75 – volume: 60 start-page: 91 year: 2004 ident: 6220_CR12 publication-title: Int. J. Comput. 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| Title | Retail Product Classification on Distinct Distribution of Training and Evaluation Data |
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