Extracting Representative Images of Tourist Attractions from Flickr by Combining an Improved Cluster Method and Multiple Deep Learning Models

Extracting representative images of tourist attractions from geotagged photos is beneficial to many fields in tourist management, such as applications in touristic information systems. This task usually begins with clustering to extract tourist attractions from raw coordinates in geotagged photos. However, most existing cluster methods are limited in the accuracy and granularity of the places of interest, as well as in detecting distinct tags, due to its primary consideration of spatial relationships. After clustering, the challenge still exists for the task of extracting representative images within the geotagged base image data, because of the existence of noisy photos occupied by a large area proportion of humans and unrelated objects. In this paper, we propose a framework containing an improved cluster method and multiple neural network models to extract representative images of tourist attractions. We first propose a novel time- and user-constrained density-joinable cluster method (TU-DJ-Cluster), specific to photos with similar geotags to detect place-relevant tags. Then we merge and extend the clusters according to the similarity between pairs of tag embeddings, as trained from Word2Vec. Based on the clustering result, we filter noise images with Multilayer Perceptron and a single-shot multibox detector model, and further select representative images with the deep ranking model. We select Beijing as the study area. The quantitative and qualitative analysis, as well as the questionnaire results obtained from real-life tourists, demonstrate the effectiveness of this framework.

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ВЫБОР МОДЕЛИ БИОЛОГИЧЕСКОЙ НЕЙРОННОЙ СЕТИ ДЛЯ СЕГМЕНТАЦИИ ИЗОБРАЖЕНИЯ БИОЖИДКОСТНОЙ ПОВЕРХНОСТИ

Вестник КРАУНЦ. Физико-математические науки ◽

10.26117/2079-6641-2019-26-1-78-93 ◽

2019 ◽

pp. 78-93

Author(s):

М.Е. Семенов ◽

Т.Ю. Заблоцкая

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Network Architecture ◽

Real Life ◽

Image Data ◽

Network Models ◽

Digital Processing ◽

Neural Network Models ◽

Biological Neural Network ◽

Delay Differential

In the paper, the biological neural network models are analyzed with a purpose to solve the problems of segmentation and pattern recognition when applied to the bio-liquid facies obtained by the cuneiform dehydration method. The peculiarities of the facies’ patterns and the key steps of their digital processing are specified in the frame of the pattern recognition. Feasibility of neural network techniques for the different image data level digital processing is reviewed as well as for image segmentation. The real-life biological neural network architecture concept is described using the mechanisms of the electrical input-output membrane voltage and both induced and endogenic (spontaneous) activities of the neural clusters when spiking. The mechanism of spike initiation is described for metabotropic and ionotropic receptive clusters with the nature of environmental exciting impact specified. Also, the mathematical models of biological neural networks that comprise ot only functional nonlinearities but the hysteretic ones are analyzed and the reasons are given for preference of the mathematical model with delay differential equations is chosen providing its applicability for modeling a single neuron and neural network as well. В работе рассматривается применение моделей биологической нейронной сети для сегментации изображения фации биожидкости, полученной методом клиновидной дегидратации. Выделены основные характерные особенности, присущие паттернам фаций биожидкостей, а также основные этапы их цифровой обработки в рамках задачи распознавания образов. Проведен анализ использования искусственных нейронных сетей для цифровой обработки изображений для разных уровней представления данных; сделан обзор основных нейросетевых методов сегментации. Описан принцип построения биологически достоверных искусственных нейронных сетей, использующих механизмы изменения мембранного потенциала нейронов и учитывающих при генерации спайка как вызванную активность, так и эндогенную (спонтанную) активность нейронных кластеров. Описан механизм инициации спайка для метаботропных и ионотропных рецептивных кластеров с указанием природы запускающего внешнего воздействия. Проведен анализ существующих математических моделей биологических нейросетей, содержащих помимо обычных функциональных нелинейностей нелинейности гистерезисной природы. Сделан выбор в пользу математической модели, использующей дифференциальные уравнения с запаздыванием, которые могут быть применены как для описания отдельного биологического нейрона, так и для описания работы нейронной сети.

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Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i3.pp1681-1688 ◽

2021 ◽

Vol 23 (3) ◽

pp. 1681

Author(s):

Aditya Rajbongshi ◽

Thaharim Khan ◽

Md. Mahbubur Rahman ◽

Anik Pramanik ◽

Shah Md Tanvir Siddiquee ◽

...

Keyword(s):

Transfer Learning ◽

Image Data ◽

Network Models ◽

Plant Diseases ◽

Prevention Measures ◽

Neural Network Models ◽

Data Set ◽

Learning Techniques ◽

Overall Performance ◽

Mango Leaves

<p>The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.</p>

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Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery

Remote Sensing ◽

10.3390/rs12132136 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2136 ◽

Cited By ~ 1

Author(s):

Arun Narenthiran Veeranampalayam Sivakumar ◽

Jiating Li ◽

Stephen Scott ◽

Eric Psota ◽

Amit J. Jhala ◽

...

Keyword(s):

Object Detection ◽

Weed Management ◽

Network Models ◽

Detection Performance ◽

Single Shot ◽

Neural Network Models ◽

Weed Detection ◽

Late Season ◽

Detection Model ◽

Growing Seasons

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean Intersection over Union (IoU) and inference speed. It was found that the Faster RCNN model with 200 box proposals had similar good weed detection performance to the SSD model in terms of precision, recall, f1 score, and IoU, as well as a similar inference time. The precision, recall, f1 score and IoU were 0.65, 0.68, 0.66 and 0.85 for Faster RCNN with 200 proposals, and 0.66, 0.68, 0.67 and 0.84 for SSD, respectively. However, the optimal confidence threshold of the SSD model was found to be much lower than that of the Faster RCNN model, which indicated that SSD might have lower generalization performance than Faster RCNN for mid- to late-season weed detection in soybean fields using UAV imagery. The performance of the object detection model was also compared with patch-based CNN model. The Faster RCNN model yielded a better weed detection performance than the patch-based CNN with and without overlap. The inference time of Faster RCNN was similar to patch-based CNN without overlap, but significantly less than patch-based CNN with overlap. Hence, Faster RCNN was found to be the best model in terms of weed detection performance and inference time among the different models compared in this study. This work is important in understanding the potential and identifying the algorithms for an on-farm, near real-time weed detection and management.

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Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks

Journal of Imaging ◽

10.3390/jimaging7080146 ◽

2021 ◽

Vol 7 (8) ◽

pp. 146

Author(s):

Joshua Ganter ◽

Simon Löffler ◽

Ron Metzger ◽

Katharina Ußling ◽

Christoph Müller

Keyword(s):

Neural Network ◽

Neural Networks ◽

Virtual Environments ◽

Semantic Information ◽

Synthetic Data ◽

Image Data ◽

Network Models ◽

Neural Network Models ◽

Real World Data ◽

Virtual Domain

Collecting real-world data for the training of neural networks is enormously time- consuming and expensive. As such, the concept of virtualizing the domain and creating synthetic data has been analyzed in many instances. This virtualization offers many possibilities of changing the domain, and with that, enabling the relatively fast creation of data. It also offers the chance to enhance necessary augmentations with additional semantic information when compared with conventional augmentation methods. This raises the question of whether such semantic changes, which can be seen as augmentations of the virtual domain, contribute to better results for neural networks, when trained with data augmented this way. In this paper, a virtual dataset is presented, including semantic augmentations and automatically generated annotations, as well as a comparison between semantic and conventional augmentation for image data. It is determined that the results differ only marginally for neural network models trained with the two augmentation approaches.

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Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

10.21203/rs.3.rs-34596/v2 ◽

2021 ◽

Author(s):

Debanjan Konar ◽

Bijaya Ketan Panigrahi ◽

Siddhartha Bhattacharyya ◽

Nilanjan Dey ◽

Richard Jiang

Keyword(s):

Automatic Segmentation ◽

Feature Learning ◽

Image Data ◽

Network Models ◽

Ct Images ◽

Neural Network Models ◽

Learning Network ◽

Novel Coronavirus ◽

Neighborhood Topology ◽

Lung Ct

Abstract Infection of Novel Coronavirus 2019 (COVID-19) on lung cells and human respiratory systems have raised real concern to the human lives during the current pandemic spread across the world. Recent observations on CT images of human lungs infected by COVID-19 is a challenging task for the researchers in finding suitable image patterns for automatic diagnosis. In this paper, a novel semi-supervised shallow learning network model comprising Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) with Fully Connected (FC) layers is proposed for automatic segmentation followed by patch-based classifications on segmented lung CT images for the diagnosis of COVID-19 disease. The PQIS-Net model is incorporated for fully automated segmentation of lung CT scan images obviating pre-trained convolutional neural network models for feature learning. The PQIS-Net model comprises a trinity of layered structures of quantum bits inter-connected through rotation gates using an 8-connected second-order neighborhood topology for the segmentation of wide variation of local intensities of the CT images. Intensive experiments have been carried out on two publicly available lung CT image data sets thereby achieving promising segmentation outcome and diagnosis efficiency (F1-score and AUC) while compared with the state of the art pre-trained convolutional based models.

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Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior

Advances in Computational Intelligence and Robotics - Applied Artificial Higher Order Neural Networks for Control and Recognition ◽

10.4018/978-1-5225-0063-6.ch007 ◽

2016 ◽

pp. 174-191 ◽

Cited By ~ 2

Author(s):

Sarat Chandra Nayak ◽

Bijan Bihari Misra ◽

Himansu Sekhar Behera

Keyword(s):

Neural Networks ◽

Stock Market ◽

Learning Process ◽

Real Life ◽

Network Models ◽

Higher Order ◽

Market Behavior ◽

Neural Network Models ◽

Efficient Prediction ◽

Higher Order Neural Networks

This chapter presents two higher order neural networks (HONN) for efficient prediction of stock market behavior. The models include Pi-Sigma, and Sigma-Pi higher order neural network models. Along with the traditional gradient descent learning, how the evolutionary computation technique such as genetic algorithm (GA) can be used effectively for the learning process is also discussed here. The learning process is made adaptive to handle the noise and uncertainties associated with stock market data. Further, different prediction approaches are discussed here and application of HONN for time series forecasting is illustrated with real life data taken from a number of stock markets across the globe.

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Two-Dimensional Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2015/491587 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Bo Jia ◽

Dong Li ◽

Zhisong Pan ◽

Guyu Hu

Keyword(s):

Extreme Learning Machine ◽

Empirical Studies ◽

Back Propagation ◽

Image Data ◽

Network Models ◽

Support Vector ◽

Two Dimensional ◽

Neural Network Models ◽

Efficiency And Effectiveness ◽

Learning Machine

Extreme learning machine (ELM) has achieved wide attention due to faster learning speed compared with conventional neural network models like support vector machine (SVM) and back-propagation (BP) networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM) based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm.

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Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.56.2.19 ◽

2021 ◽

Vol 56 (2) ◽

pp. 235-248

Author(s):

Fatchul Arifin ◽

Herjuna Artanto ◽

Nurhasanah ◽

Teddy Surya Gunawan

Keyword(s):

Neural Network ◽

Detection System ◽

Network Models ◽

Single Shot ◽

Neural Network Models ◽

X Ray ◽

Detection Model ◽

Shot Detection ◽

Average Accuracy ◽

Chest X Ray

COVID-19 is a new disease with a very rapid and tremendous spread. The most important thing needed now is a COVID-19 early detection system that is fast, easy to use, portable, and affordable. Various studies on desktop-based detection using Convolutional Neural Networks have been successfully conducted. However, no research has yet applied mobile-based detection, which requires low computational cost. Therefore, this research aims to produce a COVID-19 early detection system based on chest X-ray images using Convolutional Neural Network models to be deployed in mobile applications. It is expected that the proposed Convolutional Neural Network models can detect COVID-19 quickly, economically, and accurately. The used architecture is MobileNet's Single Shot Detection. The advantage of the Single Shot Detection MobileNet models is that they are lightweight to be applied to mobile-based devices. Therefore, these two versions will also be tested, which one is better. Both models have successfully detected COVID-19, normal, and viral pneumonia conditions with an average overall accuracy of 93.24% based on the test results. The Single Shot Detection MobileNet V1 model can detect COVID-19 with an average accuracy of 83.7%, while the Single Shot Detection MobileNet V2 Single Shot Detection model can detect COVID-19 with an average accuracy of 87.5%. Based on the research conducted, it can be concluded that the approach to detecting chest X-rays of COVID-19 can be detected using the MobileNet Single Shot Detection model. Besides, the V2 model shows better performance than the V1. Therefore, this model can be applied to increase the speed and affordability of COVID-19 detection.

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Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

Agriculture ◽

10.3390/agriculture11111111 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1111

Author(s):

Abozar Nasirahmadi ◽

Ulrike Wilczek ◽

Oliver Hensel

Keyword(s):

Neural Network ◽

Sugar Beet ◽

Convolutional Neural Network ◽

Imaging System ◽

Vision System ◽

Mechanical Damage ◽

Image Data ◽

Network Models ◽

Neural Network Models ◽

Convolutional Network

Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.

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