scholarly journals Hybrid System for Mobile Image Recognition through Convolutional Neural Networks and Discrete Graphical Models

2014 ◽  
pp. 365-376
Author(s):  
William Raveane ◽  
María Angélica González Arrieta
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Graphical Models ◽  
Hybrid System

scholarly journals SelectScale: Mining More Patterns from Images via Selective and Soft Dropout

2020 ◽  
Author(s):  
Zhengsu Chen ◽  
Jianwei Niu ◽  
Xuefeng Liu ◽  
Shaojie Tang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Small Proportion ◽  
Regularization Methods ◽  
Correct Classification

Convolutional neural networks (CNNs) have achieved remarkable success in image recognition. Although the internal patterns of the input images are effectively learned by the CNNs, these patterns only constitute a small proportion of useful patterns contained in the input images. This can be attributed to the fact that the CNNs will stop learning if the learned patterns are enough to make a correct classification. Network regularization methods like dropout and SpatialDropout can ease this problem. During training, they randomly drop the features. These dropout methods, in essence, change the patterns learned by the networks, and in turn, forces the networks to learn other patterns to make the correct classification. However, the above methods have an important drawback. Randomly dropping features is generally inefficient and can introduce unnecessary noise. To tackle this problem, we propose SelectScale. Instead of randomly dropping units, SelectScale selects the important features in networks and adjusts them during training. Using SelectScale, we improve the performance of CNNs on CIFAR and ImageNet.

Weighted pooling for image recognition of deep convolutional neural networks

2018 ◽  
Vol 22 (S4) ◽  
pp. 9371-9383 ◽  
Author(s):  
Xiaoning Zhu ◽  
Qingyue Meng ◽  
Bojian Ding ◽  
Lize Gu ◽  
Yixian Yang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Deep Convolutional Neural Networks

scholarly journals Image recognition in the artificial agriculture officer

2018 ◽  
Vol 7 (3.3) ◽  
pp. 119
Author(s):  
B Lokesh ◽  
Ravoori Charishma ◽  
Natuva Hiranmai
Keyword(s):  
Neural Networks ◽  
Information Retrieval ◽  
Search Engine ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Crop Production ◽  
Android Application ◽  
Weather Patterns ◽  
Crop Diseases ◽  
The Right

Farmers face a multitude of problems nowadays such as lower crop production, tumultuous weather patterns, and crop infections. All of these issues can be solved if they have access to the right information. The current methods of information retrieval, such as search engine lookup and talking to an Agriculture Officer, have multiple defects. A more suitable solution, that we are proposing, is an android application, available at all times, that can give succinct answers to any question a farmer may pose. The application will include an image recognition component that will be able to recognize a variety of crop diseases in the case that the farmer does not know what he is dealing with and is unable to describe it.  Image recognition is the ability of a computer to recognize and distinguish between different objects, and is actually a much harder problem to solve than it seems. We are using Tensorflow, a tool that uses convolutional neural networks, to implement it  

scholarly journals A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Konobu Kimura ◽  
Yoko Tabe ◽  
Tomohiko Ai ◽  
Ikki Takehara ◽  
Hiroshi Fukuda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Diagnostic System ◽  
Learning System ◽  
Automated Image Analysis ◽  
Image Analysis System ◽  
Cell Image ◽  
Extreme Gradient Boosting

Abstract Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndromes (MDS) are hematopoietic neoplasms characterized by dysplastic and ineffective hematopoiesis, which diagnosis is mainly based on morphological findings of PB and bone marrow. We developed an automated diagnostic support system of MDS by combining an automated blood cell image-recognition system using a deep learning system (DLS) powered by convolutional neural networks (CNNs) with a decision-making system using extreme gradient boosting (XGBoost). The DLS of blood cell image-recognition has been trained using datasets consisting of 695,030 blood cell images taken from 3,261 PB smears including hematopoietic malignancies. The DLS simultaneously classified 17 blood cell types and 97 morphological features of such cells with >93.5% sensitivity and >96.0% specificity. The automated MDS diagnostic system successfully differentiated MDS from aplastic anemia (AA) with high accuracy; 96.2% of sensitivity and 100% of specificity (AUC 0.990). This is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated diagnostic systems for various hematological disorders.

scholarly journals Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Qi Zhao ◽  
Shuchang Lyu ◽  
Boxue Zhang ◽  
Wenquan Feng
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Big Data ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Large Scale ◽  
Fog Computing ◽  
Feature Extractor ◽  
Benchmark Datasets ◽  
Classification Tasks

Convolutional neural networks (CNNs) are becoming more and more popular today. CNNs now have become a popular feature extractor applying to image processing, big data processing, fog computing, etc. CNNs usually consist of several basic units like convolutional unit, pooling unit, activation unit, and so on. In CNNs, conventional pooling methods refer to 2×2 max-pooling and average-pooling, which are applied after the convolutional or ReLU layers. In this paper, we propose a Multiactivation Pooling (MAP) Method to make the CNNs more accurate on classification tasks without increasing depth and trainable parameters. We add more convolutional layers before one pooling layer and expand the pooling region to 4×4, 8×8, 16×16, and even larger. When doing large-scale subsampling, we pick top-k activation, sum up them, and constrain them by a hyperparameter σ. We pick VGG, ALL-CNN, and DenseNets as our baseline models and evaluate our proposed MAP method on benchmark datasets: CIFAR-10, CIFAR-100, SVHN, and ImageNet. The classification results are competitive.

Performance Comparison of Deep Convolutional Neural Networks for Vespa Image Recognition

2019 ◽  
Vol 34 (3) ◽  
pp. 207-215 ◽  
Author(s):  
Cheol-Hee Lee ◽  
Yoon-Ju Jeong ◽  
Taeho Kim ◽  
Jae-Hyeon Park ◽  
Seongbin Bak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Performance Comparison ◽  
Deep Convolutional Neural Networks
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