DidMatTech 2020 Conference
Using SL methods for skin segmentation
Comparison between SVM, KNN, NB, DT and LR in the RGB and YCbCr color spaces with and without dropping duplicated records for different evaluation criteria
https://www.researchgate.net/publication/341281794_Supervised_learning_methods_for_skin_segmentation_classification
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document summarizes a research paper on automatic text detection and removal from images using morphological operations and image inpainting. It presents a technique for detecting text regions using morphological filters, edge detection, thresholding and connected component analysis. Text regions are then removed by generating a mask and applying exemplar-based image inpainting to fill in the masked regions. The technique is shown to accurately detect and remove text from sample images, leaving intact the underlying image content.
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.
Image to Text Converter PPT. PPT contains step by step algorithms/methods to which we can convert images in to text , specially contains algorithms for images which contains human handwritting, can convert writting in to text, img to text.
This document discusses optical character recognition for vehicle number plates. It begins with an overview of how the process works, taking an image of a car, locating the number plate area, and using OCR to recognize the characters. It then describes the steps involved in more detail, including image division, detecting the number plate area, recognizing and parsing the plate, and applying OCR to the characters. An example of the process is provided with images. It also discusses techniques for recognizing characters, such as template matching and distance measurement, and provides limitations and references.
Text Detection and Recognition: A ReviewIRJET Journal
This document reviews different approaches for text detection and recognition from images. It discusses two main methods: stepwise methods that separate detection and recognition into distinct stages, and integrated methods that share information between stages. The key stages are discussed as text detection and localization, classification, segmentation, and text recognition. A variety of approaches are analyzed for each stage, including their advantages and disadvantages. Finally, applications of text detection and recognition technologies are mentioned.
Data entry is a time consuming and erroneous procedure in its nature. In addition, validity
check of submitted information is not easier than retyping it. In a mega-corporation like Kanoon
Farhangi Amoozesh, there are almost no way to control the authenticity of students' educational
background. By the virtue of fast computer architectures, optical character recognition, a.k.a.
OCR, systems have become viable. Unfortunately, general-purpose OCR systems like Google's
Tesseract are not handful because they don't have any a-priori information about what they are
reading. In this paper the authors have taken a in-depth look on what has done in the field of
OCR in the last 60 years. Then, a custom-made system adapted to the problem is presented
which is way more accurate than general purpose OCRs. The developed system reads more than
60 digits per second. As shown in the Results section, the accuracy of the devised method is
reasonable enough to be exposed in public use.
Vehicle License Plate Recognition (VLPR) is an important system for harmonious traffic. Moreover this system is helpful in many fields and places as private and public entrances, parking lots, border control and theft control. This paper presents a new framework for Sudanese VLPR system. The proposed framework uses Multi Objective Particle Swarm Optimization (MOPSO) and Connected Component Analysis (CCA) to extract the license plate. Horizontal and vertical projection will be used for character segmentation and the final recognition stage is based on the Artificial Immune System (AIS). A new dataset that contains samples for the current shape of Sudanese license plates will be used for training and testing the proposes framework.
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document summarizes a research paper on automatic text detection and removal from images using morphological operations and image inpainting. It presents a technique for detecting text regions using morphological filters, edge detection, thresholding and connected component analysis. Text regions are then removed by generating a mask and applying exemplar-based image inpainting to fill in the masked regions. The technique is shown to accurately detect and remove text from sample images, leaving intact the underlying image content.
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.
Image to Text Converter PPT. PPT contains step by step algorithms/methods to which we can convert images in to text , specially contains algorithms for images which contains human handwritting, can convert writting in to text, img to text.
This document discusses optical character recognition for vehicle number plates. It begins with an overview of how the process works, taking an image of a car, locating the number plate area, and using OCR to recognize the characters. It then describes the steps involved in more detail, including image division, detecting the number plate area, recognizing and parsing the plate, and applying OCR to the characters. An example of the process is provided with images. It also discusses techniques for recognizing characters, such as template matching and distance measurement, and provides limitations and references.
Text Detection and Recognition: A ReviewIRJET Journal
This document reviews different approaches for text detection and recognition from images. It discusses two main methods: stepwise methods that separate detection and recognition into distinct stages, and integrated methods that share information between stages. The key stages are discussed as text detection and localization, classification, segmentation, and text recognition. A variety of approaches are analyzed for each stage, including their advantages and disadvantages. Finally, applications of text detection and recognition technologies are mentioned.
Data entry is a time consuming and erroneous procedure in its nature. In addition, validity
check of submitted information is not easier than retyping it. In a mega-corporation like Kanoon
Farhangi Amoozesh, there are almost no way to control the authenticity of students' educational
background. By the virtue of fast computer architectures, optical character recognition, a.k.a.
OCR, systems have become viable. Unfortunately, general-purpose OCR systems like Google's
Tesseract are not handful because they don't have any a-priori information about what they are
reading. In this paper the authors have taken a in-depth look on what has done in the field of
OCR in the last 60 years. Then, a custom-made system adapted to the problem is presented
which is way more accurate than general purpose OCRs. The developed system reads more than
60 digits per second. As shown in the Results section, the accuracy of the devised method is
reasonable enough to be exposed in public use.
A NOVEL FEATURE SET FOR RECOGNITION OF SIMILAR SHAPED HANDWRITTEN HINDI CHARA...cscpconf
This document describes a study that uses machine learning algorithms to recognize similar shaped handwritten Hindi characters. It extracts 85 features from each character image based on geometry. Four machine learning algorithms (Bayesian Network, RBFN, MLP, C4.5 Decision Tree) are trained on datasets containing samples of a target character pair and evaluated based on precision, misclassification rate, and model build time. Feature selection techniques are also used to reduce the feature set dimensionality before classification. Experimental results show that different algorithms perform best depending on the feature set and number of samples used for training.
A Survey On Thresholding Operators of Text Extraction In VideosCSCJournals
The document describes a technique for text detection in video frames that uses morphological operations. It involves four phases: 1) image enhancement to improve edge detection, 2) edge extraction using morphological operations, 3) labeling connected text regions, and 4) extracting text using Hough transforms. The technique is evaluated using quantitative metrics like boundary precision-recall and volume precision-recall to measure the accuracy of detected text boundaries and regions compared to ground truth. Experimental results show the technique can accurately detect and extract text from video frames.
Vehicle logo recognition using histograms of oriented gradient descriptor and...TELKOMNIKA JOURNAL
Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we requirea quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by histograms of oriented gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.
This document discusses a system for extracting text from images. It begins with an introduction describing the need for such a system. It then covers related work on text detection techniques. The proposed method involves converting images to grayscale, binarization, connected component analysis, horizontal/vertical projections, reconstruction and using OCR for recognition. Applications discussed include wearable devices, video coding, image indexing and license plate recognition. While the system is robust, OCR recognition of noisy extracted text remains a challenge.
Data Mining Based Skin Pixel Detection Applied On Human Images: A Study PaperIJERA Editor
Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multi-view face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
This document discusses text detection and character recognition from images. It begins with an introduction and then discusses the aims, objectives, motivation and problem statement. It reviews relevant literature on segmentation and recognition techniques. The document then describes the methodology used, including preprocessing, segmentation using vertical projections and connected components, and recognition using pixel counting, projections, template matching, Fourier descriptors and heuristic filters. It presents results from four experiments comparing different segmentation and recognition methods. The discussion analyzes results and limitations. The conclusion finds that segmentation works best with connected components while recognition works best with template matching, Fourier descriptors and heuristic filters.
Iwsm2014 an analogy-based approach to estimation of software development ef...Nesma
The document discusses fuzzy analogy, a technique for software effort estimation that can handle categorical data. It introduces fuzzy analogy and fuzzy k-modes clustering. Fuzzy k-modes is used to cluster similar software projects from a repository based on categorical attributes into homogeneous groups. Fuzzy analogy then assesses the similarity between projects based on their membership to clusters and estimates the effort of a new project as a weighted average of similar past projects' efforts. The document evaluates fuzzy analogy on 194 projects from the ISBSG repository selected based on data quality and attributes criteria.
Analysis of data is an important task in data managements systems. Many mathematical tools are used in data analysis. A new division of data management has appeared in machine learning, linear algebra, an optimal tool to analyse and manipulate the data. Data science is a multi-disciplinary subject that uses scientific methods to process the structured and unstructured data to extract the knowledge by applying suitable algorithms and systems. The strength of linear algebra is ignored by the researchers due to the poor understanding. It powers major areas of Data Science including the hot fields of Natural Language Processing and Computer Vision. The data science enthusiasts finding the programming languages for data science are easy to analyze the big data rather than using mathematical tools like linear algebra. Linear algebra is a must-know subject in data science. It will open up possibilities of working and manipulating data. In this paper, some applications of Linear Algebra in Data Science are explained.
Projection Profile Based Number Plate Localization and Recognition csandit
This paper proposes algorithms to localize vehicle
number plates from natural background
images, to segment the characters from the localize
d number plates and to recognize the
segmented characters. The reported system is tested
on a dataset of 560 sample images
captured with different background under various il
luminations. The performance accuracy of
the proposed system has been calculated at each sta
ge, which is 97.1%, 95.4% and 95.72% for
localisation & extraction, character segmentation a
nd character recognition respectively. The
proposed method is also capable of localising and r
ecognising multiple number plates in
images.
Text detection and recognition from natural sceneshemanthmcqueen
Text characters in natural scenes and surroundings provide us with valuable information about the place and even provide us with some legal/important information. Hence it’s very important for us to detect such text and recognise them which helps a lot. But , it’s not really easy to recognize those text information because of the diverse backgrounds and fonts used for the text. In this paper, a method is proposed to extract the text information from the surroundings. First, a character descriptor is designed with existing standard detectors and descriptors. Then, character structure is modeled at each character class by designing stroke configuration maps.In natural scenes , the text part is generally found on nearby sign boards and other objects. The extraction of such text is difficult because of noisy backgrounds and diverse fonts and text sizes. But many applications have been proven to be efficient in extraction of text from surroundings. For this , the method of text extraction is divided into two processes;
Text detection
Text recognition
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.
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Da...CrimsonpublishersTTEFT
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Data by Chih Huang Yen in Trends in Textile Engineering & Fashion Technology
FUNCTION OF RIVAL SIMILARITY IN A COGNITIVE DATA ANALYSIS Maxim Kazantsev
The document discusses the use of a rival similarity function (FRiS) in cognitive data analysis and machine learning algorithms. FRiS measures the similarity of an object to one object over another, and accounts for locality, normality, invariance and other properties. The authors describe how FRiS can be used to improve algorithms for tasks like classification, feature selection, filling in missing data, and ordering objects. They provide examples of algorithms like FRiS-Class that apply FRiS to problems involving clustering and taxonomy. Evaluation on real datasets shows these FRiS-based algorithms outperform other common methods.
Introduction to image processing and pattern recognitionSaibee Alam
this power point presentation provides a brief introduction to image processing and pattern recognition and its related research papers including conclusion
Machine learning workshop, session 3.
- Data sets
- Machine Learning Algorithms
- Algorithms by Learning Style
- Algorithms by Similarity
- People to follow
This document summarizes research on improving image classification results using neural networks. It compares common image classification methods like support vector machines (SVM) and K-nearest neighbors (KNN). It then evaluates the performance of multilayer perceptron (MLP) neural networks and radial basis function (RBF) neural networks on image classification. The document tests various configurations of MLP and RBF networks on a dataset containing 2310 images across 7 classes. It finds that a MLP network with two hidden layers of 10 neurons each achieves the best results, with an average accuracy of 98.84%. This is significantly higher than the 84.47% average accuracy of RBF networks and outperforms KNN classification as well. The research concludes that neural
This document summarizes a project on recognizing handwritten digits using machine learning classifiers. The researchers used the MNIST dataset and preprocessed the images before extracting features. They then applied Naive Bayes and Logistic Regression classifiers and evaluated their performance based on accuracy and confusion matrices. Logistic Regression significantly outperformed Naive Bayes. Regularization was also investigated for Logistic Regression, with cross-validation used to select the optimal regularization parameter.
Probability density estimation using Product of Conditional ExpertsChirag Gupta
This document discusses probability density estimation using a product of conditional experts model. It summarizes that density estimation constructs a probability distribution function from observed data to understand the underlying pattern. A product of conditional experts model is proposed, where simple classification models like logistic regression are used as experts to estimate the conditional probability. The experts are combined by multiplying their probabilities. The model is trained using gradient ascent to maximize the log probability. When evaluated on artificial and real datasets, the product of conditional experts model is shown to learn distributions close to the true distributions and generalize better than linear and non-linear baseline models. The document also explores applying the model to outlier detection.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
This article aims to classify texts and predict the categories of occurrences, through the study of Artificial Intelligence models, using Machine Learning and Deep Learning for the classification of texts and analysis of predictions, suggesting the best option with the smallest error.
The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
A NOVEL FEATURE SET FOR RECOGNITION OF SIMILAR SHAPED HANDWRITTEN HINDI CHARA...cscpconf
This document describes a study that uses machine learning algorithms to recognize similar shaped handwritten Hindi characters. It extracts 85 features from each character image based on geometry. Four machine learning algorithms (Bayesian Network, RBFN, MLP, C4.5 Decision Tree) are trained on datasets containing samples of a target character pair and evaluated based on precision, misclassification rate, and model build time. Feature selection techniques are also used to reduce the feature set dimensionality before classification. Experimental results show that different algorithms perform best depending on the feature set and number of samples used for training.
A Survey On Thresholding Operators of Text Extraction In VideosCSCJournals
The document describes a technique for text detection in video frames that uses morphological operations. It involves four phases: 1) image enhancement to improve edge detection, 2) edge extraction using morphological operations, 3) labeling connected text regions, and 4) extracting text using Hough transforms. The technique is evaluated using quantitative metrics like boundary precision-recall and volume precision-recall to measure the accuracy of detected text boundaries and regions compared to ground truth. Experimental results show the technique can accurately detect and extract text from video frames.
Vehicle logo recognition using histograms of oriented gradient descriptor and...TELKOMNIKA JOURNAL
Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we requirea quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by histograms of oriented gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.
This document discusses a system for extracting text from images. It begins with an introduction describing the need for such a system. It then covers related work on text detection techniques. The proposed method involves converting images to grayscale, binarization, connected component analysis, horizontal/vertical projections, reconstruction and using OCR for recognition. Applications discussed include wearable devices, video coding, image indexing and license plate recognition. While the system is robust, OCR recognition of noisy extracted text remains a challenge.
Data Mining Based Skin Pixel Detection Applied On Human Images: A Study PaperIJERA Editor
Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multi-view face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
This document discusses text detection and character recognition from images. It begins with an introduction and then discusses the aims, objectives, motivation and problem statement. It reviews relevant literature on segmentation and recognition techniques. The document then describes the methodology used, including preprocessing, segmentation using vertical projections and connected components, and recognition using pixel counting, projections, template matching, Fourier descriptors and heuristic filters. It presents results from four experiments comparing different segmentation and recognition methods. The discussion analyzes results and limitations. The conclusion finds that segmentation works best with connected components while recognition works best with template matching, Fourier descriptors and heuristic filters.
Iwsm2014 an analogy-based approach to estimation of software development ef...Nesma
The document discusses fuzzy analogy, a technique for software effort estimation that can handle categorical data. It introduces fuzzy analogy and fuzzy k-modes clustering. Fuzzy k-modes is used to cluster similar software projects from a repository based on categorical attributes into homogeneous groups. Fuzzy analogy then assesses the similarity between projects based on their membership to clusters and estimates the effort of a new project as a weighted average of similar past projects' efforts. The document evaluates fuzzy analogy on 194 projects from the ISBSG repository selected based on data quality and attributes criteria.
Analysis of data is an important task in data managements systems. Many mathematical tools are used in data analysis. A new division of data management has appeared in machine learning, linear algebra, an optimal tool to analyse and manipulate the data. Data science is a multi-disciplinary subject that uses scientific methods to process the structured and unstructured data to extract the knowledge by applying suitable algorithms and systems. The strength of linear algebra is ignored by the researchers due to the poor understanding. It powers major areas of Data Science including the hot fields of Natural Language Processing and Computer Vision. The data science enthusiasts finding the programming languages for data science are easy to analyze the big data rather than using mathematical tools like linear algebra. Linear algebra is a must-know subject in data science. It will open up possibilities of working and manipulating data. In this paper, some applications of Linear Algebra in Data Science are explained.
Projection Profile Based Number Plate Localization and Recognition csandit
This paper proposes algorithms to localize vehicle
number plates from natural background
images, to segment the characters from the localize
d number plates and to recognize the
segmented characters. The reported system is tested
on a dataset of 560 sample images
captured with different background under various il
luminations. The performance accuracy of
the proposed system has been calculated at each sta
ge, which is 97.1%, 95.4% and 95.72% for
localisation & extraction, character segmentation a
nd character recognition respectively. The
proposed method is also capable of localising and r
ecognising multiple number plates in
images.
Text detection and recognition from natural sceneshemanthmcqueen
Text characters in natural scenes and surroundings provide us with valuable information about the place and even provide us with some legal/important information. Hence it’s very important for us to detect such text and recognise them which helps a lot. But , it’s not really easy to recognize those text information because of the diverse backgrounds and fonts used for the text. In this paper, a method is proposed to extract the text information from the surroundings. First, a character descriptor is designed with existing standard detectors and descriptors. Then, character structure is modeled at each character class by designing stroke configuration maps.In natural scenes , the text part is generally found on nearby sign boards and other objects. The extraction of such text is difficult because of noisy backgrounds and diverse fonts and text sizes. But many applications have been proven to be efficient in extraction of text from surroundings. For this , the method of text extraction is divided into two processes;
Text detection
Text recognition
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.
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Da...CrimsonpublishersTTEFT
An Intelligent Skin Color Detection Method based on Fuzzy C-Means with Big Data by Chih Huang Yen in Trends in Textile Engineering & Fashion Technology
FUNCTION OF RIVAL SIMILARITY IN A COGNITIVE DATA ANALYSIS Maxim Kazantsev
The document discusses the use of a rival similarity function (FRiS) in cognitive data analysis and machine learning algorithms. FRiS measures the similarity of an object to one object over another, and accounts for locality, normality, invariance and other properties. The authors describe how FRiS can be used to improve algorithms for tasks like classification, feature selection, filling in missing data, and ordering objects. They provide examples of algorithms like FRiS-Class that apply FRiS to problems involving clustering and taxonomy. Evaluation on real datasets shows these FRiS-based algorithms outperform other common methods.
Introduction to image processing and pattern recognitionSaibee Alam
this power point presentation provides a brief introduction to image processing and pattern recognition and its related research papers including conclusion
Machine learning workshop, session 3.
- Data sets
- Machine Learning Algorithms
- Algorithms by Learning Style
- Algorithms by Similarity
- People to follow
This document summarizes research on improving image classification results using neural networks. It compares common image classification methods like support vector machines (SVM) and K-nearest neighbors (KNN). It then evaluates the performance of multilayer perceptron (MLP) neural networks and radial basis function (RBF) neural networks on image classification. The document tests various configurations of MLP and RBF networks on a dataset containing 2310 images across 7 classes. It finds that a MLP network with two hidden layers of 10 neurons each achieves the best results, with an average accuracy of 98.84%. This is significantly higher than the 84.47% average accuracy of RBF networks and outperforms KNN classification as well. The research concludes that neural
This document summarizes a project on recognizing handwritten digits using machine learning classifiers. The researchers used the MNIST dataset and preprocessed the images before extracting features. They then applied Naive Bayes and Logistic Regression classifiers and evaluated their performance based on accuracy and confusion matrices. Logistic Regression significantly outperformed Naive Bayes. Regularization was also investigated for Logistic Regression, with cross-validation used to select the optimal regularization parameter.
Probability density estimation using Product of Conditional ExpertsChirag Gupta
This document discusses probability density estimation using a product of conditional experts model. It summarizes that density estimation constructs a probability distribution function from observed data to understand the underlying pattern. A product of conditional experts model is proposed, where simple classification models like logistic regression are used as experts to estimate the conditional probability. The experts are combined by multiplying their probabilities. The model is trained using gradient ascent to maximize the log probability. When evaluated on artificial and real datasets, the product of conditional experts model is shown to learn distributions close to the true distributions and generalize better than linear and non-linear baseline models. The document also explores applying the model to outlier detection.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
selection of relevant feature from a given set of feature is one of the important issues in the field of
data mining as well as classification. In general the dataset may contain a number of features however it is not
necessary that the whole set features are important for particular analysis of decision making because the
features may share the common information‟s and can also be completely irrelevant to the undergoing
processing. This generally happen because of improper selection of features during the dataset formation or
because of improper information availability about the observed system. However in both cases the data will
contain the features that will just increase the processing burden which may ultimately cause the improper
outcome when used for analysis. Because of these reasons some kind of methods are required to detect and
remove these features hence in this paper we are presenting an efficient approach for not just removing the
unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information
theory to detect the information gain from each feature and minimum span tree to group the similar features
with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the
algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the
results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the
performances of the classifier.
This article aims to classify texts and predict the categories of occurrences, through the study of Artificial Intelligence models, using Machine Learning and Deep Learning for the classification of texts and analysis of predictions, suggesting the best option with the smallest error.
The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
ADABOOST ENSEMBLE WITH SIMPLE GENETIC ALGORITHM FOR STUDENT PREDICTION MODELijcsit
Predicting the student performance is a great concern to the higher education managements.This
prediction helps to identify and to improve students' performance.Several factors may improve this
performance.In the present study, we employ the data mining processes, particularly classification, to
enhance the quality of the higher educational system. Recently, a new direction is used for the improvement
of the classification accuracy by combining classifiers.In thispaper, we design and evaluate a fastlearning
algorithm using AdaBoost ensemble with a simple genetic algorithmcalled “Ada-GA” where the genetic
algorithm is demonstrated to successfully improve the accuracy of the combined classifier performance.
The Ada-GA algorithm proved to be of considerable usefulness in identifying the students at risk early,
especially in very large classes. This early prediction allows the instructor to provide appropriate advising
to those students. The Ada/GA algorithm is implemented and tested on ASSISTments dataset, the results
showed that this algorithm hassuccessfully improved the detection accuracy as well as it reduces the
complexity of computation.
Automated software testing cases generation framework to ensure the efficienc...Sheikh Monirul Hasan
Automated Software Testing Cases Generation Framework to Ensure the Efficiency of the Gesture Recognition Systems by Sheikh Monirul Hasan. This is a research work for creating a standard or benchmark for testing gesture recognition system software. sheikh monirul Hasan is the first author of the research paper, the complete work summary of the research we tried to discuss in the presentation slide. so there have many kinds of software engineering think and how we get a quality product specially for gesture recognition system.
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
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Supervised learning methods for skin segmentation based on color pixel classification
1. Introduction
Background
Experiments
Results and Conclusion
Supervised learning methods for skin segmentation
based on color pixel classification
XXXIII. DidMatTech 2020
Ahmad Taan and Zakarya Farou
Eötvös Loránd
Tudományegyetem
June 26, 2020
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
2. Introduction
Background
Experiments
Results and Conclusion
Table of Contents
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
3. Introduction
Background
Experiments
Results and Conclusion
Skin Segmentation
Applications
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
4. Introduction
Background
Experiments
Results and Conclusion
Skin Segmentation
Applications
Skin Segmentation
Skin Segmentation
Extraction of skin regions from colored images for further image
processing
Color Pixel Classification
Relying on the color of individual pixels to classify them into skin
and non-skin
Only 0.02% of data records in the RGB skin data set where
found common between skin and non-skin
Due to the complex mathematical relations between color
components, using SL methods is proposed
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
5. Introduction
Background
Experiments
Results and Conclusion
Skin Segmentation
Applications
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
6. Introduction
Background
Experiments
Results and Conclusion
Skin Segmentation
Applications
Applications
Figure 1: Computer Vision1
Figure 2: Tumor Detection2
1
[https://www.youtube.com/watch?v=IkgM1b1HLK0&feature=emb_title]
2
[https://www.visualsonics.com/sites/default/files/styles/manual_crop/public/media-files/5R-48hrs-UNMI
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
7. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
8. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
SL Algorithms
Figure 3: Machine Learning Algorithms
Machine Learning (ML)
A form of artificial intelligence
that enables a system to learn
from data rather than through
explicit programming
Supervised Learning (SL)
A machine is trained on labelled
data to produce a model that can
predict labels for new unlabelled
inputs
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
9. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Support Vector Machine (SVM)
Figure 4: Optimal hyperplane separating
data set classes
The goal is to find the
optimal separating
hyperplane which maximizes
the margin of the training
data
Kernelling can be used to
map the data into higher
dimensions if the data is not
linearly separable
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
10. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
K-Nearest Neighbors (KNN)
Figure 5: KNN with K=5
Classifying new inputs by
considering classes of the K
nearest neighbors
It is a lazy learning algorithm
as it memories the labelled
data set entries and
compares them to new
inputs at the prediction time
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
11. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Naïve Bayes (NB)
Bayes’ Theorem
P(y|X) =
P(X|y) × P(y)
P(X)
(1)
Where:
X = (x1, x2, x3, ..., xN) is the
features vector
y is the output class label
The goal is to find y that
maximizes P(y|X) for a
given X
Naïve comes from the
assumption of independence
between features so that
P(X|y) =
P(x1|y) × P(x2|y) ×
P(x3|y) × ... × P(xN|y)
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
12. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Decision Tree (DT)
Root
Node
Leaf
Node
Internal
Node
Internal
Node
Leaf
Node
Leaf
Node
Leaf
Node
Figure 6: Decision Tree
Nodes are split successively
into smaller nodes by
applying threshold tests
starting from the root node
until reaching the leaf nodes
where class labels are
determined
It mimics the human logic of
thinking based on
if-then-else rules structure
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
13. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Logistic Regression (LR)
y =
0 if σ(yL(X)) < φ
1 else
(2)
yL = β0 + β1x1 + β2x2 + ... + βnxN (3)
σ (x) =
1
1 + e−x
(4)
Where:
y is the output class label
yL is the linear model
X = (x1, x2, x3, ..., xN) is the features vector
φ is the threshold value
σ is the sigmoid (logistic) function
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
14. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Logistic Regression (LR)
Figure 7: LR vs Linear Regression
The machine learns the
lineal model parameters βi
by considering the sigmoid
function values as the
predicted values of class
labels
Equation (2) is the LR
equation that is used to
predict class labels for new
unlabelled data records
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
15. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
16. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Color Spaces
Representing color information with numerical values
RGB defines color by Red, Green, and Blue components. Each
value carries intensity (luminance) and color (chrominance)
information.
YCbCr separates luminance (Y ) and chrominance (Cb and Cr)
which is considered more perceptual.
RGB to YCbCr Conversion
Y =
77
256
R +
150
256
G +
29
256
B (5)
Cb = B − Y (6)
Cr = R − Y (7)
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
17. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
18. Introduction
Background
Experiments
Results and Conclusion
SL Algorithms
Color Spaces
Related Works
Related Works
Some researchers focused on color models to improve skin
detection accuracy, whereas others argued it is irrelevant.
A comparison between RGB, YCbCr, and HSV color spaces
was done to prove that an optimal skin model can be achieved
for each color space.
Some methods rely on statistics and probability concepts,
such as using look-up tables of skin color probabilities where
no mathematical calculations are involved in contrast to
distance-based approaches that make execution time
drastically higher
Skin region detection using ML algorithms such as SVM and
convolutional neural networks (CNN)
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
19. Introduction
Background
Experiments
Results and Conclusion
Data Set
Preprocessing
SL Training Configuration
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
20. Introduction
Background
Experiments
Results and Conclusion
Data Set
Preprocessing
SL Training Configuration
Data Set
The skin segmentation data set is publicly available in the UCI
repository
Records are RGB values of pixels taken randomly from human
face images with binary labels referring to skin (1) and non-skin
(0)
Sample size is 245057 with 21% skin and 79% non-skin samples
The data set includes duplicated records with 51444 unique
ones (28% skin and 72% non-skin)
Only 11 distinct RGB tuples refer to both skin and non-skin at
the same time
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
21. Introduction
Background
Experiments
Results and Conclusion
Data Set
Preprocessing
SL Training Configuration
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
22. Introduction
Background
Experiments
Results and Conclusion
Data Set
Preprocessing
SL Training Configuration
RGB Density Plots
Figure 8: RGB Density Plots of Skin and Non-skin
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
23. Introduction
Background
Experiments
Results and Conclusion
Data Set
Preprocessing
SL Training Configuration
YCbCr Density Plots
Figure 9: YCbCr Density Plots of Skin and Non-skin
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
24. Introduction
Background
Experiments
Results and Conclusion
Data Set
Preprocessing
SL Training Configuration
Pair-plots of RGB and YCbCr
Figure 10: RGB and YCbCr Pair-plots of Skin (in orange) and Non-skin (in green)
Without Duplicates
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
25. Introduction
Background
Experiments
Results and Conclusion
Data Set
Preprocessing
SL Training Configuration
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
26. Introduction
Background
Experiments
Results and Conclusion
Data Set
Preprocessing
SL Training Configuration
SL Training Configuration
The sklearn library implementations of the selected SL methods
were used with the default parameters.
Classifier Algorithm Parameters
SVM sklearn.svm.LinearSVC loss=squared_hinge C=1
KNN sklearn.neighbors.KNeighborsClassifier n_neighbors=5 p=2
NB sklearn.naive_bayes.GaussianNB var_smoothing=1e-9
DT sklearn.tree.DecisonTreeClassifier criterion=gini
LR sklearn.linear_model.LogisticRegression penalty=l2
Table 1: Parameters settings of the classifiers used in the experiments
The models were trained on the data records in the RGB and
YCbCr color spaces, with and without duplicates filtering.
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
27. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
28. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Threshold Metrics
Threshold metrics are concerned about quantifying the error in
prediction.
Predicted as Positive Predicted as Negative
Actually Positive True Positives (TP) False Negatives (FN)
Actually Negative False Positives (FP) True Negatives (TN)
Table 2: Confusion matrix
Metric Definition
Accuracy (ACC) (TP + TN)/(TP + FP + FN + TN)
Precision (P) TP/(TP + FP)
Recall (R) TP/(TP + FN)
F1-Score (F1) P × R/(P + R)
Table 3: Binary threshold metrics
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
29. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ranking Metrics
Ranking metrics are concerned about class separation. They are
quantified by the Area Under Curve concept (AUC), where the curves are
generated by changing the discrimination threshold of the models.
Figure 11: ROC and PR curves
FPR =
FP
FP + TN
(8)
TPR =
TP
TP + FN
(9)
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
30. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
31. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Experimental Results
Model
With duplicates Without duplicates Color
spaceTP FP TN FN TP FP TN FN
SVC 16292 7422 186776 34567 751 1623 35167 13903
RGB
KNN 49749+
908 193290 1110−
14362+
721 36069 292−
NB 37115 5202 188996 13744 10789 3608 33182 3865
DT 45864 901−
193297+
4995 13338 467−
36323+
1316
LR 40236 11585 182613 10623 8720 5333 31457 5934
SVC 31163 11555 182643 19696 8055 6370 30420 6599
YCbCr
KNN 49999+
659 193539 860−
14366+
537 36253 288−
NB 46163 1669 192529 4696 13408 321 36469 1246
DT 46779 220−
193978+
4080 13673 188−
36602+
981
LR 40199 11470 182728 10660 8707 5342 31448 5947
SVC 30362 19114 175084 20497 2931 7310 29480 11723
CbCr
KNN 47519+
355 193843 3340−
14010+
283 36507 644−
NB 46172 2194 192004 4687 13275 327 36463 1379
DT 47334 296−
193902+
3525 13341 239−
36551+
1313
LR 37600 12618 181580 13259 4959 5738 31052 9695
Table 4: Confusion Matrix Entries for all the experiments
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
32. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Experimental Results
Table 4 shows that KNN is the best in detecting the
minority class while DT in detecting the majority class.
In table 2, DT appears only once as the best classifier, so the
confusion matrix is not sufficient for the decision.
The confusion matrix can be used to derive threshold metrics.
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
33. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Experimental results
Model
With duplicates Without duplicates Color
space
ACC F1
ROC-
AUC
PR-
AUC
ACC F1
ROC-
AUC
PR-
AUC
SVC 82.87 35.9 90.8 68.07 69.82 6.67 79.32 51.59
RGB
KNN 99.18 97.98 99.18 98.03 98.03 96.59 98.93 96.64
NB 92.27 77.49 93.61 84.45 85.47 73.27 93.13 85.9
DT 97.59 93.2 94.86 90.53 96.53 93.47 94.92 90.59
LR 90.94 77.63 94.59 70.51 78.1 59.28 85.79 63.7
SVC 87.25 58.85 94.93 71.25 74.79 41.87 85.95 63.52
YCbCr
KNN 99.38 98.49 99.4 98.39 98.4 97.2 98.97 96.81
NB 97.4 92.71 99.75 98.51 96.95 94.24 99.6 98.57
DT 98.25 95.25 95.95 93.24 97.73 95.84 96.45 94.04
LR 90.97 77.66 94.58 70.44 78.06 59.15 85.79 63.68
SVC 83.84 48.95 94.39 67.19 63 11.63 83.81 60.68
CbCr
KNN 98.49 95.85 97.37 95.46 98.2 96.78 98.46 96.71
NB 97.19 92.21 99.75 98.55 96.68 93.8 99.62 98.47
DT 98.44 95.94 96.7 94.4 96.98 94.46 95.69 92.8
LR 89.44 73.88 94.05 67.89 70 33.78 84.74 62.02
Table 5: Summary of experimental results considering different metrics
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
34. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Experimental Results
Table 5 shows that KNN and NB share the best values for all
the metrics, so they are used to test the effect of color spaces and
duplicates.
YCbCr is the best color space overall.
The intensity (Y) is important for KNN but has negligible effect on
NB.
Duplicates dismissal gives worse results for KNN but slightly
changes the results of NB up and down.
KNN is the best considering threshold metrics (99.38%
accuracy and 98.49% F1-score) while NB is the best in terms of
ranking metrics (99.75% ROC-AUC and 98.57% PR-AUC).
KNN is the best overall as it gives comparable results to NB in
terms of ranking metrics.
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
35. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Experimental results
KNN is the worst in terms of execution time as it involves a
lot of mathematical calculations at the prediction time, which is not
preferred in real-time applications.
Figure 12: Graphical representation of the achieved results in terms of execution time
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
36. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Index
1 Introduction
Skin Segmentation
Applications
2 Background
SL Algorithms
Color Spaces
Related Works
3 Experiments
Data Set
Preprocessing
SL Training Configuration
4 Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
37. Introduction
Background
Experiments
Results and Conclusion
Evaluation Metrics
Experimental Results
Conclusion
Conclusion
YCbCr is better than RGB for skin segmentation.
The intensity (Y) importance is relevant to the algorithm used,
for instance, KNN performs better considering the intensity (Y), but
NB is almost not influenced by it.
Duplicates’ influence is also relevant to the algorithm used.
KNN performs better with duplicates while NB almost gives even
results.
The best SL model in almost all the cases considered is KNN.
Evaluation metrics are crucial for finding the superior SL model.
KNN is the best considering threshold metrics while NB is the best
in terms of ranking metrics.
Execution time is an important factor, KNN algorithm is the
worst in this regard.
Ahmad Taan and Zakarya Farou Supervised learning methods for skin segmentation based o
38. Thank you
This project has been supported by Telekom Innovation Laboratories (T-Labs),
the Research and Development unit of Deutsche Telekom.