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Max-min convolutional neural networks for image classification

Michael Blot, Matthieu Cord, Nicolas Thome
2016 2016 IEEE International Conference on Image Processing (ICIP)  
For our method we notice the fact that max(ReLU (X)), max(ReLU (−X)) = ReLU (max(X)), ReLU (−min(X)) where X is a vector.  ...  For a while, state of the art algorithms for image classification were based on bag of words (BoW) models [1] [2] . Those algorithms build a visual dictionary from local image descriptors.  ... 
doi:10.1109/icip.2016.7533046 dblp:conf/icip/BlotCT16 fatcat:3xynymgws5c4jkeaok7jdznaye

An Alternative Practice of Tropical Convolution to Traditional Convolutional Neural Networks [article]

Shiqing Fan, Liu Liying, Ye Luo
2021 arXiv   pre-print
in conventional convolutional layers are replaced by additions and min/max operations respectively.  ...  In the experiments, we test and analyze several different architectures of TCNNs for image classification tasks in comparison with similar-sized conventional CNNs.  ...  Introduction In recent years, convolutional neural networks (CNNs) have been widely used in computer vision field of machine learning [1] tasks related to images and videos, such as image classification  ... 
arXiv:2103.02096v2 fatcat:qhua2gc7ijcebjhbayadksoe7i

Low Quality Retinal Image Recognition Based on Convolutional Neural Network

Ge Yangzu, Zhang Guicang, Wang Jing
2020 Journal of Mathematics and Informatics  
Finally, the Support vector machine is used for convolutional neural network output recognition to achieve low quality image recognition.  ...  In order to solve the problem of low accuracy of traditional image recognition methods for low quality retinal images, a low quality retinal image recognition Algorithm based on convolutional neural network  ...  Also, the authors are thankful to the anonymous referees for their valuable comments.  ... 
doi:10.22457/jmi.v19a05178 fatcat:pfeoimofrvfypf42uzwpqaa3ai

Continuous Human Action Detection Based on Wearable Inertial Data [article]

Xia Gong, Yan Lu, Haoran Wei
2021 arXiv   pre-print
Experiment results show the best performance was achieved by image based inertial feature with convolution neural network, which got 51.1% F1 score.  ...  Based on inertial sensor and C-MHAD smart TV gesture recognition dataset, this paper utilized different inertial sensor feature formats, then compared the performance with different deep neural network  ...  One of the formats is descried above, using that image as a input to convolution neural network, then getting the classification results.  ... 
arXiv:2112.06091v1 fatcat:c7di54wxonarpo5xkqmkaxb53m

Calculating Screen Time of Characters in a Video Using Convolutional Neural Networks

2020 International Journal of Advanced Trends in Computer Science and Engineering  
Supervised Learning Methodology with Convolutional Neural Network as the classifier is used in this paper.  ...  In this paper, we propose a methodology to calculate the screen time of characters appearing in the video by using the classification-count method.  ...  In this paper, we implemented concepts of image classification using Convolution Neural Networks(ConvNet's).  ... 
doi:10.30534/ijatcse/2020/180952020 fatcat:pq3hlkq7ljegbdvdld74vdbmui

PLDANet: Reasonable Combination of PCA and LDA Convolutional Networks

Caicai Zhang, Mei Mei, Zhuolin Mei, Junkang Zhang, Anyuan Deng, Chenglang Lu
2022 International Journal of Computers Communications & Control  
For example, PCANet and LDANet adopts Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA) to learn convolutional kernels separately.  ...  It is not reasonable to adopt LDA to learn filter kernels in each convolutional layer, local features of images from different classes may be similar, such as background areas.  ...  (DNN) [5] and Convolutional Neural Network (CNN) [21] .  ... 
doi:10.15837/ijccc.2022.2.4541 fatcat:u73he5l3jjbbhmx4unfpreu5iy


Ervin Miloš, Aliaksei Kolesau, Dmitrij Šešok
2018 Mokslas: Lietuvos Ateitis  
Nowadays, to solve traffic sign recognition problem, convolutional neural networks (CNN) can be adopted for its high performance well proved for computer vision applications.  ...  The convolutional neural network is an extension which is very effective in image recognition and classification.  ...  For the future work, more convolutional neural network architectures by changing hyper-parameters will be considered for testing.  ... 
doi:10.3846/mla.2018.6947 fatcat:3cqqr7bndbh37iiqsk3ysgekca

Ultrasonic Image Diagnosis of Liver and Spleen Injury Based on a Double-Channel Convolutional Neural Network

Maorui He, Rui Zhang, Shuni Liu, Yansong Tan, Yang Zeng, Wenqing Wu
2021 Wireless Communications and Mobile Computing  
neural network was proposed.  ...  Finally, the external edge feature and internal texture feature are used as two input channels of the convolutional neural network, respectively, to classify and identify ultrasonic images of liver and  ...  Acknowledgments This study was supported by the Development of Remote Intelligent Management System for Ultrasonic Critical Value in General Practice (No. 2019MSXM090).  ... 
doi:10.1155/2021/2827011 fatcat:yb6ouogee5dgdhh5qhsphh2mqa

Robust Real-Time Face Detection Using Hybrid Neural Networks [chapter]

Ho-Joon Kim, Juho Lee, Hyun-Seung Yang
2006 Lecture Notes in Computer Science  
We present an extended version of fuzzy min-max (FMM) neural network which can be used not only for feature analysis but also for pattern classification.  ...  A modified convolutional neural network(CNN) is used to extract translation invariant feature maps for face detection.  ...  We introduce a multi-stage face detection method using a hybrid neural network which combines a convolutional neural network and a fuzzy min-max neural network.  ... 
doi:10.1007/11816102_76 fatcat:yxwk6q7xafcvhey3dvrzj66yci

Sparse Coral Classification Using Deep Convolutional Neural Networks [article]

Mohamed Elawady
2015 arXiv   pre-print
We present an efficient sparse classification for coral species using supervised deep learning method called Convolutional Neural Networks (CNNs).  ...  , we also experiment state-of-art preprocessing underwater algorithms for image enhancement and color normalization and color conversion adjustment.  ...  Image Normalization y = (max o − min o ) * (x − min i ) (max i − min i ) + min o .  ... 
arXiv:1511.09067v1 fatcat:dgm3yw274bg2zp2qkvodvfuqvm

Deep Learning-based framework for Autism functional MRI Image Classification

Xin Yang, Saman Sarraf, Ning Zhang
2018 Journal of the Arkansas Academy of Science  
By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls.  ...  The purpose of this paper is to introduce the deep learning-based framework LeNet-5 architecture and implement experiments for functional MRI image classification of Autism spectrum disorder.  ...  The fully connected layer is the hidden layer of Artificial Neural Networks, which fully connects the features from convolution layer and pooling layer to produce the output for image classification.  ... 
doi:10.54119/jaas.2018.7214 fatcat:dvdpeenhbrejlntskhvakctdta

Deep Learning Techniques for the Classification of Colorectal Cancer Tissue

Min-Jen Tsai, Yu-Han Tao
2021 Electronics  
Firstly, we trained a neural network and compared the network architecture optimizers. Secondly, we modified the parameters of the network layer to optimize the superior architecture.  ...  However, since histological images contain multiple tissue types and characteristics, classification is still challenging.  ...  Acknowledgments: The authors thank to National Center for High-performance Computing (NCHC) of National Applied Research Laboratories (NARLabs) in Taiwan for providing computational and storage resources  ... 
doi:10.3390/electronics10141662 fatcat:gasciiedyfb2nbrer2kfmtxvxa

Comparative Analysis of Scene Classification Methods for Remotely Sensed Images using Various Convolutional Neural Network

P. Deepan, L.R. Sudha
2021 EAI Endorsed Transactions on Cognitive Communications  
Over the past few decades, with the rapid development of deep learning models particularly Convolutional Neural Network (CNN), the performance of RSI scene classifications has been drastically improved  ...  Remote sensing image (RSI) scene classification has received growing attention from the research community in recent days.  ...  Fine tuning the hyper parameter of RSISC-16 Model This section describes the RSISC-16 deep CNN model for scene classification of remote sensing images using deep convolutional neural networks.  ... 
doi:10.4108/eai.11-2-2021.168714 fatcat:d7iy5l73zfap5berccpmilcffm

Reconfiguration layers of convolutional neural network for fundus patches classification

Wahyudi Setiawan, Moh. Imam Utoyo, Riries Rulaningtyas
2021 Bulletin of Electrical Engineering and Informatics  
Convolutional neural network (CNN) is a method of supervised deep learning.  ...  Data used for classification is MESSIDOR and Retina Image Bank that have 2,080 patches.  ...  Bulletin of Electr Eng & Inf ISSN: 2302-9285  Reconfiguration layers of convolutional neural network for fundus… (Wahyudi Setiawan) 385 Figure 1. CNN backpropagation training [25] 2.1.  ... 
doi:10.11591/eei.v10i1.1974 fatcat:i7umku4pfnfr7o3r72hkjfuo2q

Classification Using 3D Point Cloud and 2D Image on Abstract Objects

Zhenming Yang, Guillermo Goldsztein
2021 Journal of student research  
We also observed that the neural network based on images performs worse than that based on point clouds.  ...  This paper compared neural networks with similar structures using 3D point clouds and 2D images on the same objects.  ...  Secondly, I would also like to thank my family and friends, Sam Liu and James Li, for peer-reviewing the paper. Thanks again to all who supported me.  ... 
doi:10.47611/jsrhs.v10i3.2222 fatcat:62jpxvrwrbdwdgm5ctmxgacozu
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