scholarly journals Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition

2021 ◽  
Vol 18 (3) ◽  
pp. 961
Author(s):  
Yi Chung ◽  
Chih-Ang Chou ◽  
Chih-Yang Li
Keyword(s):  
Deep Learning ◽  
Tree Species ◽  
Species Recognition ◽  
Recognition System ◽  
Prototype System ◽  
Individual Plant ◽  
Learning Approaches ◽  
Attention Model ◽  
Central Attention ◽  
Plant Recognition

Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural networks (CNN), their performances are still far from the requirements of an in-field scenario. First, we propose a central attention concept that helps focus on the target instead of backgrounds in the image for tree species recognition. It could prevent model training from confused vision by establishing a dual path CNN deep learning framework, in which the central attention model combined with the CNN model based on InceptionV3 were employed to automatically extract the features. These two models were then learned together with a shared classification layer. Experimental results assessed the effectiveness of our proposed approach which outperformed each uni-path alone, and existing methods in the whole plant recognition system. Additionally, we created our own tree image database where each photo contained a wealth of information on the entire tree instead of an individual plant organ. Lastly, we developed a prototype system of an online/offline available tree species identification working on a consumer mobile platform that can identify the tree species not only by image recognition, but also detection and classification in real-time remotely.

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
The Internet ◽  
Learning Approaches ◽  
The Public ◽  
Sensory Data ◽  
The Internet Of Things

The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor-based human behaviour. Next, we present the multi-modal sensory data and include information for the public databases which can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing perspectives into the future.

An Improved Arabic Handwritten Recognition System using Deep Support Vector Machines

2016 ◽  
Vol 7 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Mohamed Elleuch ◽  
Monji Kherallah
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Arabic Characters ◽  
Rbf Kernel ◽  
Data Points ◽  
Handwritten Recognition

Deep learning algorithms, as a machine learning algorithms developed in recent years, have been successfully applied in various domains of computer vision, such as face recognition, object detection and image classification. These Deep algorithms aim at extracting a high representation of the data via multi-layers in a deep hierarchical structure. However, to the authors' knowledge, these deep learning approaches have not been extensively studied to recognize Arabic Handwritten Script (AHS). In this paper, they present a deep learning model based on Support Vector Machine (SVM) named Deep SVM. This model has an inherent ability to select data points crucial to classify good generalization capabilities. The deep SVM is constructed by a stack of SVMs allowing to extracting/learning automatically features from the raw images and to perform classification as well. The Multi-class SVM with an RBF kernel, as non-linear discriminative features for classification, was chosen and tested on Handwritten Arabic Characters Database (HACDB). Simulation results show the effectiveness of the proposed model.

Tree species recognition system based on macroscopic image analysis

2016 ◽  
Vol 51 (2) ◽  
pp. 431-444 ◽  
Author(s):  
Imanurfatiehah Ibrahim ◽  
Anis Salwa Mohd Khairuddin ◽  
Mohamad Sofian Abu Talip ◽  
Hamzah Arof ◽  
Rubiyah Yusof
Keyword(s):  
Image Analysis ◽  
Tree Species ◽  
Species Recognition ◽  
Recognition System

scholarly journals Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4838
Author(s):  
Philip Gouverneur ◽  
Frédéric Li ◽  
Wacław M. Adamczyk ◽  
Tibor M. Szikszay ◽  
Kerstin Luedtke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Feature Learning ◽  
Recognition System ◽  
Extraction Methods ◽  
Feature Engineering ◽  
Learning Approaches ◽  
Physiological Sensors ◽  
Pain Recognition

While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches have proposed automatic pain evaluation systems using machine learning models trained with data coming from behavioural or physiological sensors. Although yielding promising results, machine learning studies for sensor-based pain recognition remain scattered and not necessarily easy to compare to each other. In particular, the important process of extracting features is usually optimised towards specific datasets. We thus introduce a comparison of feature extraction methods for pain recognition based on physiological sensors in this paper. In addition, the PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for heat-induced pain in 52 subjects, is introduced. In total, five different approaches including techniques based on feature engineering and feature learning with deep learning are evaluated on the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature engineering approaches can still compete with deep learning approaches in terms of performance. (2) More complex deep learning architectures do not yield better performance compared to simpler ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based annotations to build a robust pain recognition system.

An Improved Arabic Handwritten Recognition System Using Deep Support Vector Machines

2018 ◽  
pp. 656-678 ◽  
Author(s):  
Mohamed Elleuch ◽  
Monji Kherallah
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Arabic Characters ◽  
Rbf Kernel ◽  
Data Points ◽  
Handwritten Recognition

Deep learning algorithms, as a machine learning algorithms developed in recent years, have been successfully applied in various domains of computer vision, such as face recognition, object detection and image classification. These Deep algorithms aim at extracting a high representation of the data via multi-layers in a deep hierarchical structure. However, to the authors' knowledge, these deep learning approaches have not been extensively studied to recognize Arabic Handwritten Script (AHS). In this paper, they present a deep learning model based on Support Vector Machine (SVM) named Deep SVM. This model has an inherent ability to select data points crucial to classify good generalization capabilities. The deep SVM is constructed by a stack of SVMs allowing to extracting/learning automatically features from the raw images and to perform classification as well. The Multi-class SVM with an RBF kernel, as non-linear discriminative features for classification, was chosen and tested on Handwritten Arabic Characters Database (HACDB). Simulation results show the effectiveness of the proposed model.

scholarly journals Large-scale Image Classification with Explainable deep learning scheme

2021 ◽  
Author(s):  
Bin Wu ◽  
Yuhong Fan ◽  
Li Mao
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Spatial Attention ◽  
Large Scale ◽  
Spatial Dimension ◽  
Main Function ◽  
Learning Approaches ◽  
Attention Model ◽  
Object Decision ◽  
The Arts

Abstract For the uncertainty and complexity in object decision making and the differences of decision makers ' reliabilities, an object decision making method based on deep learning theory is proposed. However, traditional deep learning approaches optimize the parameters in an "end-to-end" mode by annotating large amounts of data to propagate the errors backwards. The learning method could be considered to be as a "black box", which is weak in explainability. Explainability refers to an algorithm that gives a clear summary of a particular task and connects it to defined principles or principles in the human world. This paper proposes an explainable attention model consisting of channel attention module and spatial attention module. The proposed module derives attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. For different channels, the higher the weight, the higher the correlation which required more attention. The main function of spatial attention is to capture the most informative part in the local feature graph, which is a supplement to channel attention. We evaluate our proposed module based on the ImageNet-1K and Cifar-100 respectively. Experimental results show that our algorithm is superior in both accuracy and robustness compared with the state of the arts.

scholarly journals Animal Species Recognition System using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 966-971
Author(s):  
Deepthi K
Keyword(s):  
Deep Learning ◽  
Animal Species ◽  
Species Recognition ◽  
Region Of Interest ◽  
Recognition System ◽  
Test Dataset ◽  
Training Images ◽  
Learning Platform ◽  
Animal Images ◽  
Sensitivity Specificity

Animals watching is a common hobby but to identify their species requires the assistance of Animal books. To provide Animal watchers a handy tool to admire the beauty of Animals, we developed a deep learning platform to assist users in recognizing species of Animals endemic to using app named the Imagenet of Animals (IoA). Animal images were learned by a convolutional neural network (CNN) to localize prominent features in the images. First, we established and generated a bounded region of interest to the shapes and colors of the object granularities and subsequently balanced the distribution of Animals species. Then, a skip connection method was used to linearly combine the outputs of the previous and current layers to improve feature extraction. Finally, we applied the SoftMax function to obtain a probability distribution of Animals features. The learned parameters of Animals features were used to identify pictures uploaded by mobile users. The proposed CNN model with skip connections achieved higher accuracy of 99.00 % compared with the 93.98% from a CNN and 89.00% from the SVM for the training images. As for the test dataset, the average sensitivity, specificity, and accuracy were 93.79%, 96.11%, and 95.37%, respectively.

scholarly journals Augmentation Methods for Biodiversity Training Data

2019 ◽  
Vol 3 ◽  
Author(s):  
Mario Lasseck
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Data Augmentation ◽  
Species Recognition ◽  
Original Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Computer Hardware ◽  
Individual Species ◽  
Learning Approaches

The detection and identification of individual species based on images or audio recordings has shown significant performance increase over the last few years, thanks to recent advances in deep learning. Reliable automatic species recognition provides a promising tool for biodiversity monitoring, research and education. Image-based plant identification, for example, now comes close to the most advanced human expertise (Bonnet et al. 2018, Lasseck 2018a). Besides improved machine learning algorithms, neural network architectures, deep learning frameworks and computer hardware, a major reason for the gain in performance is the increasing abundance of biodiversity training data, either from observational networks and data providers like GBIF, Xeno-canto, iNaturalist, etc. or natural history museum collections like the Animal Sound Archive of the Museum für Naturkunde. However, in many cases, this occurrence data is still insufficient for data-intensive deep learning approaches and is often unbalanced, with only few examples for very rare species. To overcome these limitations, data augmentation can be used. This technique synthetically creates more training samples by applying various subtle random manipulations to the original data in a label-preserving way without changing the content. In the talk, we will present augmentation methods for images and audio data. The positive effect on identification performance will be evaluated on different large-scale data sets from recent plant and bird identification (LifeCLEF 2017, 2018) and detection (DCASE 2018) challenges (Lasseck 2017, Lasseck 2018b, Lasseck 2018c).

scholarly journals Enhanced Local Patterns Using Deep Learning Techniques for ECG Based Identity Recognition System

2021 ◽  
Author(s):  
Lotfi Mostefai ◽  
Benouis Mohamed ◽  
Denai Mouloud ◽  
Bouhamdi Merzoug
Keyword(s):  
Deep Learning ◽  
Normal Sinus Rhythm ◽  
Recognition System ◽  
Learning Approaches ◽  
Identity Recognition ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Learning Techniques ◽  
Local Patterns ◽  
Distinct Features

Abstract Electrocardiogram (ECG) signals have distinct features of the electrical activity of the heart which are unique among individuals and have recently emerged as a potential biometric tool for human identification. The paper attempts to address the problem of ECG identification based on non-fiducial approach using unsupervised classifier and a Deep Learning approaches. This work investigates the ability of local binary pattern to extract the significant pattern/feature that describes the heartbeat activity for each person’s ECG and the use of staked autoencoders and deep belief network to further enhance the extracted features and classify them based on their heartbeat activity. The proposed approach is validated using experimental datasets from two publicly available databases MIT-BIH Normal Sinus Rhythm and ECG-ID and the results demonstrate the effectiveness of this approach for ECG-based human authentication.

Sign in / Sign up

Export Citation Format

Share Document