Mobile phone recognition method based on bilinear convolutional neural network

[1]  ByoungChul Ko,et al.  Cell image classification based on ensemble features and random forest , 2011 .

[2]  Liang Ye,et al.  Unsupervised noise-robust feature extraction for aerial image classification , 2020 .

[3]  Liming Yao,et al.  An integrated method of life-cycle assessment and system dynamics for waste mobile phone management and recycling in China , 2018, Journal of Cleaner Production.

[4]  Nazmul Huda,et al.  Waste electric and electronic equipment (WEEE) management: A study on the Brazilian recycling routes , 2018 .

[5]  Huiyan Wang,et al.  Fine-grained bird recognition by using contour-based pose transfer , 2015 .

[6]  Xianlai Zeng,et al.  Uncovering the Recycling Potential of "New" WEEE in China. , 2016, Environmental science & technology.

[7]  Gengui Zhou,et al.  WEEE recycling in Zhejiang Province, China: generation, treatment, and public awareness , 2016 .

[8]  Xiaoqiang Lu,et al.  Exploiting spatial relation for fine-grained image classification , 2019, Pattern Recognit..

[9]  Maoguo Gong,et al.  Local Descriptor Learning for Change Detection in Synthetic Aperture Radar Images via Convolutional Neural Networks , 2019, IEEE Access.

[10]  Yuxin Peng,et al.  Attribute hierarchy based multi-task learning for fine-grained image classification , 2020, Neurocomputing.

[11]  Lili Jiang,et al.  Improving the accuracy of image-based forest fire recognition and spatial positioning , 2010 .

[12]  Wu Hao,et al.  Optimized CNN Based Image Recognition Through Target Region Selection , 2018 .

[13]  Chinmoy Biswas,et al.  Logo Recognition Technique using Sift Descriptor, Surf Descriptor and Hog Descriptor , 2015 .

[14]  H. Wenzel,et al.  Potential for circular economy in household WEEE management , 2017 .

[15]  Jinhui Li,et al.  Potential recycling availability and capacity assessment on typical metals in waste mobile phones: A current research study in China , 2017 .

[16]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[17]  Xiu-Shen Wei,et al.  Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition , 2016, ArXiv.

[18]  Tasbirul Islam,et al.  Reverse logistics and closed-loop supply chain of Waste Electrical and Electronic Equipment (WEEE)/E-waste: A comprehensive literature review , 2018, Resources, Conservation and Recycling.

[19]  Jitendra Malik,et al.  Analyzing the Performance of Multilayer Neural Networks for Object Recognition , 2014, ECCV.

[20]  Yu Miao,et al.  Remanufacturing strategies: a solution for WEEE problem , 2017 .

[21]  V. S. Rotter,et al.  Assessment of element-specific recycling efficiency in WEEE pre-processing , 2017 .

[22]  Subhransu Maji,et al.  Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Qi Tian,et al.  Part-Based Deep Hashing for Large-Scale Person Re-Identification , 2017, IEEE Transactions on Image Processing.

[24]  Shuicheng Yan,et al.  LG-CNN: From local parts to global discrimination for fine-grained recognition , 2017, Pattern Recognit..

[25]  Gong Cheng,et al.  P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Adam Herout,et al.  BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance , 2017, IEEE Transactions on Intelligent Transportation Systems.

[27]  Cewu Lu,et al.  Deep LAC: Deep localization, alignment and classification for fine-grained recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Wei Li,et al.  Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines , 2018, Neurocomputing.

[29]  Xudong Jiang,et al.  LBP-Based Edge-Texture Features for Object Recognition , 2014, IEEE Transactions on Image Processing.

[30]  Guangming Li,et al.  WEEE recovery strategies and the WEEE treatment status in China. , 2006, Journal of hazardous materials.

[31]  Mei Xie,et al.  ASP-CNN: aligning semantic parts for fine-grained image classification , 2019, J. Electronic Imaging.