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Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techn

Prelude Metrics Classifiers KNN Classifier Random Forest Classifier ANN Classifier SVM Classifier Datasets Used Organization of the Book Summary References A Review of Handcrafted Feature Extraction Techniques for Content-Based Image Classification Prelude Extraction of Features with Color Contents Extraction of Features with Image Binarization Extraction of Features with Image Transforms Extraction of Features with Morphological Processing Extraction of Features with Texture Content Fusion of Features Extracted with Multiple Techniques Techniques of Classification Logic-Based Algorithms Decision Trees Learning a Set of Rules Perceptron-Based Techniques Statistical Learning Algorithm Support Vector Machine Summary References Content-Based Feature Extraction: Color Averaging Prelude Block Truncation Coding Feature Extraction Using Block Truncation Coding with Color Clumps Feature Extraction Using Sorted Block Truncation Codingfor Content-Based Image Classification Comparison of Proposed Techniques Comparison with Existing Techniques Statistical Significance Summary References Content-Based Feature Extraction: Image Binarization Prelude Feature Extraction Using Mean Threshold Selection Feature Extraction with Multilevel Mean Threshold Selection Feature Extraction from Significant Bit Planes Using Mean Threshold Selection Feature Extraction from Even and Odd Image Varieties Using Mean Threshold Selection Feature Extraction with Static and Dynamic Ternary Image Maps Using Mean Threshold Selection Feature Extraction Using Local Threshold Selection Comparing the Discussed Techniques for Performance Evaluation Comparison with Existing Techniques Statistical Significance Summary References Content-Based Feature Extraction: Image Transforms Prelude Generating Partial Energy Coefficient from Transformed Images Computational Complexity for the Image Transforms Feature Extraction with Partial Energy Coefficient Discrete Cosine Transform Walsh Transform Kekre Transform Discrete Sine Transform Discrete Hartley Transform Evaluation of the Proposed Techniques Comparison with Existing Techniques Statistical Significance Summary References Content-Based Feature Extraction: Morphological Operators Prelude Top-Hat Тransform Bottom-Hat Тransform Comparison of Proposed Techniques Comparison with Existing Methods Statistical Significance Summary References Content-Based Feature Extraction: Texture Components Prelude Feature Extraction by Vector Quantization Codebook Representation Using Linde-Buzo-Grey (LBG) Algorithm Feature Extraction by Gray Level Co-occurrence Matrix (GLCM)Evaluation of Proposed Techniques Comparison with Existing Methods Statistical Significance Summary References Fusion-Based Classification: A Comparison of Early Fusion and Late Fusion Architecture for Content-Based Features Prelude Image Preprocessing Feature Extraction with Image Binarization Feature Extraction Applying Discrete Cosine Transform (DCT)Classification Framework Classification Results Summary References Future Directions: A Journey from Handcrafted Techniques to Representation Learning Prelude Representation Learning-Based Feature Extraction Image Color Averaging Techniques Binarization Techniques Image Transforms Morphological Operations Texture Analysis Multitechnique Feature Extraction for Decision Fusion-Based Classification Comparison of Cross Domain Feature Extraction Techniques Future Work References WEKA: Beginners' Tutorial

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Broad Classification of Machine Learning
Machine learning can be classified as supervised and unsupervised learning, as shown in Figure 4.6. Supervised Learning In supervised learning, we have information which can be enter variable (a) and output variable (y). We use a model (algorithm) to study the mapping characteristic FIGURE 4.7...
(Sustainable Manufacturing for Industry 4.0:An Augmented Approach)
A Review of Handcrafted Feature Extraction Techniques for Content-Based Image Classification
Prelude An enormous quantity of valuable information is accessible from the image data archived in colossal databases. Assorted sources, including entertainment media, mainstream media, defense, sports events and social media platforms, contribute readily to form these image data archives [1]....
(Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques)
Feature Extraction Using Sorted Block Truncation Codingfor Content-Based Image Classification
The comparative results are given in Table 3.2.Image processing algorithms have considered noise extending adverse influence on image data by staining it to a large extent. The fact is evident inplenty of literature that has introduced varied noise elimination techniquesto decrease the effect of noise...
(Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques)
Classification of Machine Learning
Depending on the nature of learning machine learning is classified as: • Supervised • Unsupervised • Reinforcement • Semi-supervised Supervised learning: Learning from the sample data and associated target responses and trying to make predictions for new examples comes under the category of supervised...
(Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches)
Classification of Machine Learning
Machine learning implementations are classified into three major categories, depending on the nature of the learning “signal” or “response” available to a learning system which are as follows: 1. Supervised learning: When an algorithm learns from example data and associated target responses...
(Blockchain, Big Data and Machine Learning: Trends and Applications)
Efficiently Detecting Security Threats with Deep Machine Learning
Traditional technologies used in the field of malicious file analysis are based on the matching of file signatures, also known as static signatures. However, as the range of malicious files types continues to expand, and evasion techniques become even more sophisticated, it is widely acknowledged within...
(Campus Network Architectures and Technologies)
The Machine Learning Algorithm Classifications
There are four major categorizations of the Machine Learning Algorithms, and they are as follows: 1) Supervised Learning: These types of algorithms make use of what are known as “labeled data.” This simply means that each dataset has a certain label that is associated with them. In this instance,...
(Practical AI for Cybersecurity)
Machine Learning Classification Models
Common classification methods include linear discriminant analysis (LDA), quadratic dis- criminant analysis (QDA), boosted logistic regression (in addition the standard logistic regression), decision trees, random forests, graphical models via Bayesian networks, sup- port vector machines (SVMs), and...
(Handbook of Forensic Statistics)
Machine Learning in Text Classification
ML is applied for handwriting recognition and text classification. In the recent advancement of technology, various systems have been developed for recognition of text and numbers. The MNIST dataset is used for the recognition of numbers and letters in a text. Classification-based ML models predict the...
(Intelligent Communication and Automation Systems)
Machine Learning
Machine learning and data mining often employ the same methods and overlap significantly. Machine learning, by definition, is a type of artificial intelligence (Al) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer...
(Crime and Intelligence Analysis: An Integrated Real-Time Approach)