Fine-Grained Image Recognition of Wild Mushroom Based on Multiscale Feature Guide

Zhigang Zhang; Pengfei Yu; Haiyan Li; Hongsong Li

doi:10.3788/LOP202259.1210016

[1] Wan R, Liu Z T, Wan Q Q et al. Analysis on wild mushroom poisoning in Yunnan Province from 2011 to 2017[J]. Soft Science of Health, 33, 84-86, 97(2019).

[2] Li L J, Li G Y, Xie Q T. Research progress on poisonous mushroom toxins classification and recognition[J]. Chinese Journal of Food Hygiene, 25, 383-387(2013).

[3] Grandstaff D E, Terry D O,. Rare earth element composition of Paleogene vertebrate fossils from Toadstool Geologic Park, Nebraska, USA[J]. Applied Geochemistry, 24, 733-745(2009).

[4] Zhao J, Cao M, Zhang J et al. Pathological effects of the mushroom toxin α-amanitin on BALB/c mice[J]. Peptides, 27, 3047-3052(2006).

[5] Gui M Y, He R, Guo Y H et al. Based on morphological characteristics and ITS classification and identifica-tion of Xinjiang Reed Root mushrooms by sequence[J]. Edible Fungi, 36, 14-16(2014).

[6] Zeng L K. Recognition and prevention of poisonous mushroom poisoning[J]. Forest by-Product and Speciality in China, 97-98(2008).

[7] Liu Y, Wang T, Zuo Y M. Study on recognition model for wild mushroom based on support vector machine of near infrared spectral diagnosis[J]. Food & Machinery, 32, 92-94, 112(2016).

[8] Liu B, Zhang Z D, Zhang T T. Recognition of poisonous mushrooms based on Bayesian classification[J]. Software Guide, 14, 60-62(2015).

[9] Yan Z F. Application of multi-classifier fusion based on Stacking algorithm in identification of poisonous mushrooms[D](2019).

[10] Shen R L, Huang Y L, Wen X et al. Mushroom classification based on Xception and ResNet50 models[J]. Journal of Heihe University, 11, 181-184(2020).

[11] Xiao J W, Zhao C B, Li X J et al. Research on mushroom image classification based on deep learning[J]. Software Engineering, 23, 21-26(2020).

[12] Chen D G, Yin P B et al. Research on identification of wild mushroom species based on improved Xception transfer learning[J]. Laser & Optoelectronics Progress, 58, 0810023(2021).

[13] Zhang J, Zhao H D, Li Y H et al. Classifier for recognition of fine-grained vehicle models under complex background[J]. Laser & Optoelectronics Progress, 56, 041501(2019).

[14] Feng C, Zhao N J, Yin G F et al. Recognition of waterborne pathogens based on spectral similarity analysis[J]. Acta Optica Sinica, 40, 0330002(2020).

[15] Yuan P S, Shen C J, Xu H L. Fine-grained mushroom phenotype recognition based on transfer learning and bilinear CNN[J]. Transactions of the Chinese Society for Agricultural Machinery, 52, 151-158(2021).

[16] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[17] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C], 15523970(2015).

[18] Liu J J, Hou Q B, Cheng M M et al. A simple pooling-based design for real-time salient object detection[C], 3912-3921(2019).

[19] Liu Y, Zhang X Y, Bian J W et al. SAMNet: stereoscopically attentive multi-scale network for lightweight salient object detection[J]. IEEE Transactions on Image Processing, 30, 3804-3814(2021).

[20] Stergiou A, Poppe R, Kalliatakis G. Refining activation downsampling with SoftPool[EB/OL]. https://arxiv.org/abs/2101.00440

[21] Li Y X, Yang F, Liu Z et al. Classification method of crossing vehicle based on improved residual network[J]. Laser & Optoelectronics Progress, 58, 0415009(2021).

[22] Xu Z J, Wang D. Multi-pose face recognition with two-cycle generative adversarial network[J]. Acta Optica Sinica, 40, 1910002(2020).

[23] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[24] Peng J L, Gu Y, Zhang Z et al. Document representation fused with term contribution and Word2Vec word vector[J]. Computer Engineering, 47, 62-67(2021).

[25] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[26] Selvaraju R R, Cogswell M, Das A et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision, 128, 336-359(2020).