Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
A novel method is proposed for image segmentation based on probabilistic field theory. This model assumes that the whole pixels of an image and some unknown parameters form a field. According to this model, the pixel labels are generated by a compound function of the field. The main novelty of this model is it consider the features of the pixels and the interdependent among the pixels. The parameters are generated by a novel spatially variant mixture model and estimated by expectation-maximization (EM)-
based algorithm. Thus, we simultaneously impose the spatial smoothness on the prior knowledge. Numerical experiments are presented where the proposed method and other mixture model-based methods were tested on synthetic and real world images. These experimental results demonstrate that our algorithm achieves competitive performance compared to other methods.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Real Time Myanmar Traffic Sign Recognition System using HOG and SVMijtsrd
Traffic sign recognition is one of the most important research topics for enabling autonomous vehicle driving systems. In order to be deployed in driving environments, intelligent transport system must be able to recognize and respond to exceptional road conditions such as traffic signs, highway work zones and imminent road works automatically. In this paper, Real time Myanmar Traffic Sign Recognition System RMTSRS is proposed. The incoming video stream is fed into computer vision. Then each incoming frames are segmented using color threshold method for traffic sign detection. A Histogram of Oriented Gradients HOG technique is used to extract the features from the segmented traffic sign and then RMTSRS classifies traffic sign types using Support Vector Machine SVM . The system achieves classification accuracy up to 98 . Myint Tun | Thida Lwin "Real-Time Myanmar Traffic Sign Recognition System using HOG and SVM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27929.pdfPaper URL: https://www.ijtsrd.com/computer-science/real-time-computing/27929/real-time-myanmar-traffic-sign-recognition-system-using-hog-and-svm/myint-tun
Cognitive radio networks enable a more efficient use of the radioelectric spectrum through dynamic access. Decentralized cognitive radio networks have gained popularity due to their advantages over centralized networks. The purpose of this article is to propose the collaboration between secondary users for cognitive Wi-Fi networks, in the form of two multi-criteria decision-making algorithms known as TOPSIS and VIKOR and assess their performance in terms of the number of failed handoffs. The comparative analysis is established under four different scenarios, according to the service class and the traffic level, within the Wi-Fi frequency band. The results show the performance evaluation obtained through simulations and experimental measurements, where the VIKOR algorithm has a better performance in terms of failed handoffs under different scenarios and collaboration levels.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
A novel method is proposed for image segmentation based on probabilistic field theory. This model assumes that the whole pixels of an image and some unknown parameters form a field. According to this model, the pixel labels are generated by a compound function of the field. The main novelty of this model is it consider the features of the pixels and the interdependent among the pixels. The parameters are generated by a novel spatially variant mixture model and estimated by expectation-maximization (EM)-
based algorithm. Thus, we simultaneously impose the spatial smoothness on the prior knowledge. Numerical experiments are presented where the proposed method and other mixture model-based methods were tested on synthetic and real world images. These experimental results demonstrate that our algorithm achieves competitive performance compared to other methods.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Real Time Myanmar Traffic Sign Recognition System using HOG and SVMijtsrd
Traffic sign recognition is one of the most important research topics for enabling autonomous vehicle driving systems. In order to be deployed in driving environments, intelligent transport system must be able to recognize and respond to exceptional road conditions such as traffic signs, highway work zones and imminent road works automatically. In this paper, Real time Myanmar Traffic Sign Recognition System RMTSRS is proposed. The incoming video stream is fed into computer vision. Then each incoming frames are segmented using color threshold method for traffic sign detection. A Histogram of Oriented Gradients HOG technique is used to extract the features from the segmented traffic sign and then RMTSRS classifies traffic sign types using Support Vector Machine SVM . The system achieves classification accuracy up to 98 . Myint Tun | Thida Lwin "Real-Time Myanmar Traffic Sign Recognition System using HOG and SVM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27929.pdfPaper URL: https://www.ijtsrd.com/computer-science/real-time-computing/27929/real-time-myanmar-traffic-sign-recognition-system-using-hog-and-svm/myint-tun
Cognitive radio networks enable a more efficient use of the radioelectric spectrum through dynamic access. Decentralized cognitive radio networks have gained popularity due to their advantages over centralized networks. The purpose of this article is to propose the collaboration between secondary users for cognitive Wi-Fi networks, in the form of two multi-criteria decision-making algorithms known as TOPSIS and VIKOR and assess their performance in terms of the number of failed handoffs. The comparative analysis is established under four different scenarios, according to the service class and the traffic level, within the Wi-Fi frequency band. The results show the performance evaluation obtained through simulations and experimental measurements, where the VIKOR algorithm has a better performance in terms of failed handoffs under different scenarios and collaboration levels.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videosijtsrd
The paper presents a novel algorithm for object classification in videos based on improved support vector machine (SVM) and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform. This paper presents an GA-SVM machine learning approach for real time object classification in videos. Radial distance signal features are extracted from the silhouettes of object detected in videos. The radial distance signals features are then normalized and fed into the GA-SVM model. The classification rate of 99.39% is achieved with the genetically trained SVM algorithm while 99.1% classification accuracy is achieved with the normal SVM. A comparison of this classifier with some other classifiers in terms of classification accuracy shows a better performance than other classifiers such as the normal SVM, Artificial neural network (ANN), Genetic Artificial neural network (GANN), K-nearest neighbor (K-NN) and K-Means classifiers. Akintola Kolawole G."A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd109.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/109/a-novel-ga-svm-model-for-vehicles-and-pedestrial-classification-in-videos/akintola-kolawole-g
This article aims at a new algorithm for tracking moving objects in the long term. We have tried to overcome some potential difficulties, first by a comparative study of the measuring methods of the difference and the similarity between the template and the source image. In the second part, an improvement of the best method allows us to follow the target in a robust way. This method also allows us to effectively overcome the problems of geometric deformation, partial occlusion and recovery after the target leaves the field of vision. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require a data based detection in advance. Experimental results on several difficult video sequences have proven performance advantages over many recent trackers. The developed algorithm can be employed in several applications such as video surveillance, active vision or industrial visual servoing.
Computational Geometry based Remote Networkingidescitation
In recent years wireless sensor networks (WSNs) have become one of the most
active research areas due to the bright and interesting future promised to the world of
information technology. It is an emerging field which is accomplishing much importance
because of its vast contribution in varieties of applications. Coverage is one of the important
aspects of WSNs and many approaches are introduced to maximize it. It is the key research
issue in WSN as it can be considered as the measure of the Quality of Service (QoS) of
sensing function for a sensor network. The goal of coverage is to have each location in the
physical space of interest within the sensing range of at least one sensor. By combining
computational geometry and graph theoretic techniques, specifically the Voronoi Diagram
(VD), Delaunay Triangulation (DT) and Graph Search algorithms, can solve the problem.
This paper defines some recent research approaches on coverage of WSNs using VD and
DT. Also shows how they are being utilized in various research works.
A Survey on: Hyper Spectral Image Segmentation and Classification Using FODPSOrahulmonikasharma
The Spatial analysis of image sensed and captured from a satellite provides less accurate information about a remote location. Hence analyzing spectral becomes essential. Hyper spectral images are one of the remotely sensed images, they are superior to multispectral images in providing spectral information. Detection of target is one of the significant requirements in many are assuc has military, agriculture etc. This paper gives the analysis of hyper spectral image segmentation using fuzzy C-Mean (FCM)clustering technique with FODPSO classifier algorithm. The 2D adaptive log filter is proposed to denoise the sensed and captured hyper spectral image in order to remove the speckle noise.
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentationTELKOMNIKA JOURNAL
A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.
Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...ijcsit
In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum
Clustering for Feature Selection performs the selection in two steps. Partitioning the original features
space in order to group similar features is performed using the Quantum Clustering algorithm. Then the
selection of a representative for each cluster is carried out. It uses similarity measures such as correlation
coefficient (CC) and the mutual information (MI). The feature which maximizes this information is chosen
by the algorithm
Classification of Satellite broadcasting Image and Validation Exhausting Geom...IJSRD
Classification of Land Use/Land Cover (LULC) data from satellite images is extremely remarkable to design the thematic maps for analysis of natural resources like Forest, Agriculture, Water bodies, urban areas etc. The process of Satellite Image Classification involves grouping the pixel values into significant categories and estimating areas by counting each category pixels. Manual classification by visual interpretation technique is accurate but time consuming and requires field experts. To overcome these difficulties, the present research work investigated efficient and effective automation of satellite image classification. Automated classification approaches are broadly classified in to i) Supervised Classification ii) Unsupervised Classification iii) Object Based Classification. This paper presents classification capabilities of K-Means, Parallel Pipe and Maximum Likelihood classifiers to classify multispectral spatial data (LISS-4). Using statistical inference, classified results are validated with reference data collected from field experts. Among three, Maximum Likelihood classifier (MLC) gained a significant credit in terms of getting maximum Overall accuracy and Kappa Factor.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videosijtsrd
The paper presents a novel algorithm for object classification in videos based on improved support vector machine (SVM) and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform. This paper presents an GA-SVM machine learning approach for real time object classification in videos. Radial distance signal features are extracted from the silhouettes of object detected in videos. The radial distance signals features are then normalized and fed into the GA-SVM model. The classification rate of 99.39% is achieved with the genetically trained SVM algorithm while 99.1% classification accuracy is achieved with the normal SVM. A comparison of this classifier with some other classifiers in terms of classification accuracy shows a better performance than other classifiers such as the normal SVM, Artificial neural network (ANN), Genetic Artificial neural network (GANN), K-nearest neighbor (K-NN) and K-Means classifiers. Akintola Kolawole G."A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd109.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/109/a-novel-ga-svm-model-for-vehicles-and-pedestrial-classification-in-videos/akintola-kolawole-g
This article aims at a new algorithm for tracking moving objects in the long term. We have tried to overcome some potential difficulties, first by a comparative study of the measuring methods of the difference and the similarity between the template and the source image. In the second part, an improvement of the best method allows us to follow the target in a robust way. This method also allows us to effectively overcome the problems of geometric deformation, partial occlusion and recovery after the target leaves the field of vision. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require a data based detection in advance. Experimental results on several difficult video sequences have proven performance advantages over many recent trackers. The developed algorithm can be employed in several applications such as video surveillance, active vision or industrial visual servoing.
Computational Geometry based Remote Networkingidescitation
In recent years wireless sensor networks (WSNs) have become one of the most
active research areas due to the bright and interesting future promised to the world of
information technology. It is an emerging field which is accomplishing much importance
because of its vast contribution in varieties of applications. Coverage is one of the important
aspects of WSNs and many approaches are introduced to maximize it. It is the key research
issue in WSN as it can be considered as the measure of the Quality of Service (QoS) of
sensing function for a sensor network. The goal of coverage is to have each location in the
physical space of interest within the sensing range of at least one sensor. By combining
computational geometry and graph theoretic techniques, specifically the Voronoi Diagram
(VD), Delaunay Triangulation (DT) and Graph Search algorithms, can solve the problem.
This paper defines some recent research approaches on coverage of WSNs using VD and
DT. Also shows how they are being utilized in various research works.
A Survey on: Hyper Spectral Image Segmentation and Classification Using FODPSOrahulmonikasharma
The Spatial analysis of image sensed and captured from a satellite provides less accurate information about a remote location. Hence analyzing spectral becomes essential. Hyper spectral images are one of the remotely sensed images, they are superior to multispectral images in providing spectral information. Detection of target is one of the significant requirements in many are assuc has military, agriculture etc. This paper gives the analysis of hyper spectral image segmentation using fuzzy C-Mean (FCM)clustering technique with FODPSO classifier algorithm. The 2D adaptive log filter is proposed to denoise the sensed and captured hyper spectral image in order to remove the speckle noise.
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentationTELKOMNIKA JOURNAL
A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.
Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...ijcsit
In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum
Clustering for Feature Selection performs the selection in two steps. Partitioning the original features
space in order to group similar features is performed using the Quantum Clustering algorithm. Then the
selection of a representative for each cluster is carried out. It uses similarity measures such as correlation
coefficient (CC) and the mutual information (MI). The feature which maximizes this information is chosen
by the algorithm
Classification of Satellite broadcasting Image and Validation Exhausting Geom...IJSRD
Classification of Land Use/Land Cover (LULC) data from satellite images is extremely remarkable to design the thematic maps for analysis of natural resources like Forest, Agriculture, Water bodies, urban areas etc. The process of Satellite Image Classification involves grouping the pixel values into significant categories and estimating areas by counting each category pixels. Manual classification by visual interpretation technique is accurate but time consuming and requires field experts. To overcome these difficulties, the present research work investigated efficient and effective automation of satellite image classification. Automated classification approaches are broadly classified in to i) Supervised Classification ii) Unsupervised Classification iii) Object Based Classification. This paper presents classification capabilities of K-Means, Parallel Pipe and Maximum Likelihood classifiers to classify multispectral spatial data (LISS-4). Using statistical inference, classified results are validated with reference data collected from field experts. Among three, Maximum Likelihood classifier (MLC) gained a significant credit in terms of getting maximum Overall accuracy and Kappa Factor.
Image classification as a process of assigning all pixels in the image to particular classes or themes based on spectral information represented by the digital numbers (DNs). The classified image comprises a mosaic of pixels, each of which belong to a particular theme and is a thematic map of the original image.
Approaches to Classification There are two general approaches to image classification:
Supervised Classification: It is the process of identification of classes within a remote sensing data with inputs from and as directed by the user in the form of training data, and
Unsupervised Classification: It is the process of automatic identification of natural groups or structures within a remote sensing data.
IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST...cscpconf
The growing population of elders in the society calls for a new approach in care giving. By inferring what activities elderly are performing in their houses it is possible to determine their
physical and cognitive capabilities. In this paper we show the potential of important discriminative classifiers namely the Soft-Support Vector Machines (C-SVM), Conditional Random Fields (CRF) and k-Nearest Neighbors (k-NN) for recognizing activities from sensor patterns in a smart home environment. We address also the class imbalance problem in activity recognition field which has been known to hinder the learning performance of classifiers. Cost sensitive learning is attractive under most imbalanced circumstances, but it is difficult to determine the precise misclassification costs in practice. We introduce a new criterion for selecting the suitable cost parameter C of the C-SVM method. Through our evaluation on four real world imbalanced activity datasets, we demonstrate that C-SVM based on our proposed criterion outperforms the state-of-the-art discriminative methods in activity recognition.
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESVikash Kumar
IMAGE CLASSIFICATION USING KNN, RANDOM FOREST AND SVM ALGORITHM ON GLAUCOMA DATASETS AND EXPLAIN THE ACCURACY, SENSITIVITY, AND SPECIFICITY OF EACH AND EVERY ALGORITHMS
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspec...Konstantinos Demertzis
Deep learning architectures are the most e
ective methods for analyzing and classifying Ultra-Spectral Images (USI). However, e ective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above diculties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it oers an improved training stability, high generalization performance and remarkable classification accuracy.
Abstract: Object Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories. Classification includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification. Many classification techniques have been developed for image classification. In this survey various classification techniques are considered; Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM) and Fuzzy Classification.Keywords: Image Classification, Artificial Neural Network, Decision Tree, Support Vector Machine, Fuzzy Classifier.
Title: Analysis of Classification Approaches
Author: Robin Kumar
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST...csandit
The growing population of elders in the society calls for a new approach in care giving. By
inferring what activities elderly are performing in their houses it is possible to determine their
physical and cognitive capabilities. In this paper we show the potential of important
discriminative classifiers namely the Soft-Support Vector Machines (C-SVM), Conditional
Random Fields (CRF) and k-Nearest Neighbors (k-NN) for recognizing activities from sensor
patterns in a smart home environment. We address also the class imbalance problem in activity
recognition field which has been known to hinder the learning performance of classifiers. Cost
sensitive learning is attractive under most imbalanced circumstances, but it is difficult to
determine the precise misclassification costs in practice. We introduce a new criterion for
selecting the suitable cost parameter C of the C-SVM method. Through our evaluation on four
real world imbalanced activity datasets, we demonstrate that C-SVM based on our proposed
criterion outperforms the state-of-the-art discriminative methods in activity recognition.
Predicting Students Performance using K-Median ClusteringIIRindia
The main objective of education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge of students in a particular course. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. In this paper, the K-Median method in clustering technique is used to evaluate students performance. By this task the extracted knowledge that describes students performance in end semester examination. It helps earlier in identifying the students who need special attention and allow the teacher to provide appropriate advising and coaching.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Elizabeth Buie - Older adults: Are we really designing for our future selves?
DIGITAL IMAGE ANALYSIS
1. CHAPTER FIVE
IMAGE CLASSIFICATION AND ANALYSIS
• A human analyst attempting to classify features in an image
uses the elements of visual Interpretation to identify
homogeneous groups of pixels which represent various features
or land cover classes of interest.
• Spectral pattern recognition - uses the spectral information
represented by the digital numbers in one or more spectral
bands, and attempts to classify each individual pixel based on
this spectral information.
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1
2. CONT…
Information classes – are those categories of interest that the analyst is
actually trying to identify in the imagery, such as different kinds of
crops, different forest types or tree species, different geologic units or
rock types, etc.
Spectral classes - are groups of pixels that are uniform (or near-similar)
with respect to their brightness values in the different spectral channels
of the data.
The objective is to match the spectral classes in the data to the
information classes of interest.
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4. Common classification procedures can be
broken down into two broad subdivisions based
on the method used:
I. SUPERVISED CLASSIFICATION
II. UNSUPERVISED CLASSIFICATION
III. HYBRID CLASSIFICATION
CONT…
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5. SUPERVISED CLASSIFICATION
In a supervised classification, the analyst identifies homogeneous representative
samples (referred to as training areas) of the different surface cover types (information
classes) of interest in the imagery.
The selection of appropriate training areas is based on
the analyst’s familiarity with the geographical area and
knowledge of the actual surface cover types present in the image.
Thus, the analyst is supervising the categorization of a set of specific classes.
The numerical information in all spectral bands for the pixels comprising these areas,
are used to train the computer to recognize spectrally similar areas for each class.
The computer uses special programs or algorithms to determine the numerical
signatures for each training class.
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6. Once the computer has determined the signatures for each class,
each pixel in the image is compared to these signatures and labeled as
the class it closely resembles digitally.
Thus, in a supervised classification, the analyst is first identifies the
information classes based on which it determines the spectral classes
which represent them.
In order to carry out supervised classification the analyst may have to
adopt a well defined procedure in so as to achieve a satisfactory
classification of information
CONT…
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7. STEPS REQUIRED FOR SUPERVISED CLASSIFICATION
(a) Firstly,
acquire satellite data and accompanying metadata.
Look for information regarding platform, projection, resolution, coverage,
and, importantly, meteorological conditions before and during data
acquisition.
(b) Secondly,
Chose the surface types to be mapped. Collect ground truth data with
positional accuracy (GPS).
These data are used to develop the training classes for the discriminant
analysis.
Ideally, it is best to time the ground truth data collection to coincide with
the satellite passing overhead.
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8. CONT…
(c)Thirdly,
begin the classification by performing image post-
processing techniques (corrections, image mosaics, and
enhancements).
Select pixels in the image that are representative (and
homogeneous) of the object.
If GPS field data were collected, geo-register the GPS field
plots onto the imagery and define the image training sites by
outlining the GPS polygons. A training class contains the sum
of points (pixels) or polygons (clusters of pixels)
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9. The success of a supervised classification depends upon the training data used to
identify different classes.
Hence selection of training data has to be done meticulously keeping in mind each
training data set has some specific characteristics.
These characteristics are discussed below:
Number of pixels: This is an important characteristic regarding the number of
pixels to be selected for each information class.
However, there is no guideline available, yet in general, the analyst
must ensure that sufficient number of pixels is selected.
One training area has a MMU of about 4 hectares
100 pixels per class which also should have to govern the MMU.
(10×Bands to 100×Bands) pixels per class.
1% of the total image could be the whole class samples in an image
Size: The training sets identified on the image should be large enough to provide
accurate and reliable information regarding the informational class.
Instead of selecting larger samples in a limited area of the whole image, it is
more reliable to select small samples, large in number, in a uniform distribution
throughout the image.
CONT…
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10. Shapes
Shape: It is not an important
characteristic.
However, regular shape of
training area selected provides
ease in extracting the
information from the satellite
images.
CONT…
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11. Placement: The training area should be placed in such a
way that it does not lie close to the edge of the boundary
of the information class.
Uniformity: This is one the most critical and important
characteristics of any training data for an information class.
The training data collected must exhibit uniformity or
homogeneity in the information.
If the histogram displays one peak, i.e., unimodal
frequency distribution for each spectral class, the
training data is acceptable.
If the display is multimodal distribution, then there is
variability or mixing of information and hence must be
discarded.
CONT…
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12. Location: Generally informational classes have small spectral variability, thus it is
necessary that training data are should be so located that it accounts for different
types of conditions within the image.
It is desirable that the analyst undertakes a field visit to the desired location
to clearly mark out the selected information.
In case of inaccessible or mountainous regions, aerial photographs or maps can
provide the basis for accurate delineation of training areas.
Number of training areas: The number of training areas depends upon the number
of categories to be mapped, their diversity, and the resources available for
delineating training areas.
In general, five to ten training samples per class are selected in order to
account for the spatial and spectral variability of informational class.
Selection of multiple training areas is also desirable as it may be possible that some
training areas of a class may have to be discarded later.
CONT…
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15. 1. Minimum Mean Distance –
Minimum distance to the mean is a simple computation that classifies pixels
based on their distance from the mean of the training class.
It is determined by plotting the pixel brightness and calculating its Euclidean
distance (using the Pythagorean theorem) to the unassigned pixel.
Some Classification Algorithms
Where
μck and μcl represent the mean vectors for class c measured in bands k and l
The unknown pixel is assigned to the closest class.
Any unknown pixel will definitely be assigned to one of any classes, there will
be no unclassified pixel.
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16. Pixels are assigned to the training class for which it has a
minimum distance.
The user designates a minimum distance threshold for an
acceptable distance; pixels with distance values above the
designated threshold will be classified as unknown.
Data Minimum Distance to Mean ClassificationScatter plot Training
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17. ADVANTAGES
Since every pixel is spectrally closer to either one sample mean or other so
there are no unclassified pixels.
Mathematically simple and computationally faster.
DISADVANTAGES
Pixels which should be unclassified will become classified.
Does not consider class variability or in sensitive to the different degree of class
variance in spectral data
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18. 2. Maximum Likelihood –
Maximum Likelihood is computationally complex.
It establishes the variance and covariance about the mean of the
training classes.
This algorithm then statistically calculates the probability of an
unassigned pixel belonging to each class.
The pixel is then assigned to the class for which it has the highest
probability.
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19. Equal Probability Contours are plotted
Probability of belonging to a class decreases with distance from it’s
mean point 1912/24/2019
20. ADVANTAGES
Most accurate of classifiers (if input sample have normal distribution)
because it takes the most variables.
Takes variability of classes into account.
DISADVANTAGES
An extensive equation, takes long time to compute.
It is parametric.
Tends to over classify signatures with relatively large values in the
covariance matrix.
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21. UNSUPERVISED CLASSIFICATION
In essence, it is reverse of the supervised
classification process.
Spectral classes are grouped, first, based solely on
the numerical information in the data, and are then
matched by the analyst to information classes (if
possible).
Programs called clustering algorithms are used to
determine the natural groupings or structures in the
data.
Usually, the analyst specifies how many groups or
clusters are to be looked for in the data. 12/24/2019 21
22. CONT…
Some clusters have been broken down, each of
these require a further iteration of the clustering
algorithm.
Thus, unsupervised classification is not completely
without human intervention.
However, it does not start with a pre-determined
set of classes as in a supervised classification.
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26. Steps Required for Unsupervised Classification
1) The number of classes,
2) The maximum number of iterations,
3) The maximum number of times a pixel can be moved from one
cluster to another with each iteration,
4) The minimum distance from the mean, and
5) The maximum standard deviation allowable.
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29. 29
ADVANCED IMAGE PROCESSING
SOFT (SUB-PIXEL) CLASSIFICATION
Mixed Pixel is one of the characteristic of remote sensing datasets
Additionally, pixels containing more than one landuse-land cover classes
also need to be examined
The spectral reflectance of a mixed pixel is an average of the use land
cover classes in it.
There are many algorithms developed to classify mixed pixel having
different classes in it, some and most used are:
MLC in fuzzy form
Linear Mixture Modelling
Fuzzy C-Mean Classifier
Neural Networks Classifier 12/24/2019
30. 30
ADVANCED IMAGE PROCESSING
A Neural Networks is a massively parallel distributed processor made up of simple
processing units, which has a natural tendency for storing experimental knowledge and
making it available for use. It resembles brain in two respects:
Knowledge is acquired b the network from its environment through a learning process
Interneuron connection strengths, known as synaptic weights, are used to store the
acquired knowledge
An important characteristic of Artificial Neural Networks (ANN) are:
Their non-parametric nature, which assumes no priori knowledge, particularly of the
frequency distribution of the data.
Their adaptability and their ability to produce results with classification accuracies,
that are higher than those generated by statistical classifiers,
Capabilities in handling complex datasets leading to an increasing amount of research
in the remote sensing field (Paola and Schowengerdt 1995b, Atkinson and Tatnall
1997).
Neural Networks Classifier
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31. 31
ADVANCED IMAGE PROCESSING
The rapid uptake of neural approaches in remote sensing is due mainly to their widely demonstrated ability to:
perform more accurately and rapidly than other techniques such as statistical classifiers, particularly
when the feature space is complex and the source data has nonlinear/different statistical distributions
incorporate a priori knowledge and realistic physical constraints into the analysis (Brown and Harris 1994);
incorporate different types of data (including those from different sensors) into the analysis, thus
facilitating synergistic studies (Benediktsson et al. 1993, Benediktsson and Sveinsson 1997);
process with no underlying model that assumed for the multivariate distribution of the specific data in
feature space, i.e they are distribution-free;
it does not require the data to follow the sequence of probability density function.
adapt non-linearity and can be conceived as a complex mathematical function that converts input data (e.g.,
landuse parameters such as slope, buffer distance, population density etc) to a desired output (e.g.,
growth prediction).
has better accuracy and evaluate the training and testing match before prediction.
Once the training addresses the testing requirement with the required level of accuracy, the prediction
could be performed to achieve higher accuracy
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32. 32
ADVANCED IMAGE PROCESSING
NEURAL NETWORKS ARCHITECTURE
Synapses/Connecting Links Neurons/Nodes
1
11W
1
12W
1
13W
2
11W
2
21W
2
31W
2
12W
2
22W
2
32W
1
21W
1
22W
1
23W
Weight associated with the path connecting the jth element of the ith layer to the kth element of the (i+1)th layer.
i
jkW
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33. 33
ADVANCED IMAGE PROCESSING
Input Neurons
Number of Input Neuron is determined based on the number of Bands,
Hidden Neurons
The number of nodes in the hidden layers defines the complexity and power of the
neural network model to delineate underlying relationships and structures inherent
in a dataset.
The number of hidden layer nodes has a considerable effect on both classification
accuracy and training time requirements.
The level of classification accuracy that can be produced by a neural network is
related to the generalization capabilities of that network,
That the number of nodes in the hidden layer(s) should be large enough for the
correct representation of the problem,
Parameters Affecting Neural Networks Classification
1. Number of Neurons/Nodes
I. Architectural Parameters
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34. 34
ADVANCED IMAGE PROCESSING
But at the same time low enough to have adequate generalization capabilities.
Networks that are too small cannot identify the internal structure of the data (a state
known as under fitting) and therefore produce lower classification accuracies.
Networks that are too large are Back propagating artificial neural networks in land cover
classification likely to become over specific to the training data.
Number of Neurons in the Hidden Layers are dependent on the number of nodes/ Neurons
in the input layer and summarized as follows:
where Ni represents the number of input nodes.
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35. 35
ADVANCED IMAGE PROCESSING
Ni is the number of input units, NW is the total number of weights in the network,
1. Number of training samples
The number of training samples employed at the learning stage has a significant impact on the
performance of any classifier.
Although the size of the training data is of considerable importance, the characteristics and
the distributions of the data, as well as the sampling strategy used, can affect the results.
Heuristics proposed for the computation of the optimum number of training samples:
Output Neurons
Number of Neurons in the Output Layers are dependent on the number of classes required from the
remote sensor datasets.
II. Training Parameters
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36. 36
ADVANCED IMAGE PROCESSING
2. Initial of Weights
The initial weight values define a starting location on the multi-dimensional error
surface
When large initial values are assigned to the weights, it is likely that causes the learning
process to slow down
If the initial weight values are assigned small values, the backpropagation algorithm may
operate on a very flat area around the origin of the error surface
3. Learning Rate and Momentum Factors
The learning rate determines the size of the steps taken in the search for the global
minimum of the error function in the training process
The momentum term uses the previous weight configuration to determine the direction
of search for the global minimum of the error
4. Training Iterations
The model compute through its algorithm to classify until
The error goal required is achieved or
The Maximum iteration is met
II. Training Parameters
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