Content-based image classification: efficient machine learning using robust feature extraction techniques
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Milton
CRC Press LLC
2020
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| Subjects: | |
| Online Access: | UPA01 Volltext |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (xvi, 180 Seiten) |
| ISBN: | 9780429352928 9781000280715 |
| DOI: | 10.1201/9780429352928 |
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| 505 | 8 | |a Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Author -- 1. Introduction to Content-Based Image Classification -- 1.1. Prelude -- 1.2. Metrics -- 1.2.1. Precision -- 1.2.2. True Positive (TP) Rate/Recall -- 1.2.3. Misclassification Rate (MR) -- 1.2.4. F1-Score -- 1.2.5. Accuracy -- 1.2.6. False Positive (FP) Rate -- 1.2.7. True Negative (TN) Rate -- 1.2.8. False Negative (FN) Rate -- 1.3. Classifiers -- 1.3.1. KNN Classifier -- 1.3.2. Random Forest Classifier -- 1.3.3. ANN Classifier -- 1.3.4. SVM Classifier -- 1.4. Datasets Used -- 1.4.1. Wang Dataset -- 1.4.2. Caltech Dataset -- 1.4.3. Corel Dataset -- 1.4.4. Oliva Torralba (OT-Scene) Dataset -- 1.5. Organization of the Book -- Chapter Summary -- References -- 2. A Review of Handcrafted Feature Extraction Techniques for Content-Based Image Classification -- 2.1. Prelude -- 2.2. Extraction of Features with Color Contents -- 2.3. Extraction of Features with Image Binarization -- 2.4. Extraction of Features with Image Transforms -- 2.5. Extraction of Features with Morphological Processing -- 2.6. Extraction of Features with Texture Content -- 2.7. Fusion of Features Extracted with Multiple Techniques -- 2.8. Techniques of Classification -- 2.9. Logic-Based Algorithms -- 2.9.1. Decision Trees -- 2.9.2. Learning a Set of Rules -- 2.9.3. Perceptron-Based Techniques -- 2.9.3.1 Single-Layer Perceptrons -- 2.9.3.2 Multilayer Perceptrons -- 2.9.4. Statistical Learning Algorithm -- 2.9.5. Support Vector Machine -- Chapter Summary -- References -- 3. Content-Based Feature Extraction: Color Averaging -- 3.1. Prelude -- 3.2. Block Truncation Coding -- 3.3. Feature Extraction Using Block Truncation Coding with Color Clumps -- 3.4. Code Example (MATLAB) -- 3.5. Coding Exercise | |
| 505 | 8 | |a 3.6. Feature Extraction Using Sorted Block Truncation Coding for Content-Based Image Classification -- 3.7. Code Example (MATLAB) -- 3.8. Coding Exercise -- 3.9. Comparison of Proposed Techniques -- 3.10. Comparison with Existing Techniques -- 3.11. Statistical Significance -- Chapter Summary -- References -- 4. Content-Based Feature Extraction: Image Binarization -- 4.1. Prelude -- 4.2. Feature Extraction Using Mean Threshold Selection -- 4.2.1. Feature Extraction with Multilevel Mean Threshold Selection -- 4.3. Code Example (MATLAB) -- 4.4. Coding Exercise -- 4.5. Feature Extraction from Significant Bit Planes Using Mean Threshold Selection -- 4.6. Code Example (MATLAB) -- 4.7. Coding Exercise -- 4.8. Feature Extraction from Even and Odd Image Varieties Using Mean Threshold Selection -- 4.9. Code Example (MATLAB) -- 4.10. Coding Exercise -- 4.11. Feature Extraction with Static and Dynamic Ternary Image Maps Using Mean Threshold Selection -- 4.12. Code Example (MATLAB) -- 4.13. Feature Extraction Using Local Threshold Selection -- 4.14. Code Example (MATLAB) -- 4.15. Coding Exercise -- 4.16. Comparing the Discussed Techniques for Performance Evaluation -- 4.17. Comparison with Existing Techniques -- 4.18. Statistical Significance -- Chapter Summary -- References -- 5. Content-Based Feature Extraction: Image Transforms -- 5.1. Prelude -- 5.2. Generating Partial Energy Coefficient from Transformed Images -- 5.3. Code Example (MATLAB) -- 5.4. Coding Exercise -- 5.5. Computational Complexity for the Image Transforms -- 5.6. Feature Extraction with Partial Energy Coefficient -- 5.6.1. Discrete Cosine Transform -- 5.6.2. Walsh Transform -- 5.6.3. Kekre Transform -- 5.6.4. Discrete Sine Transform -- 5.6.5. Discrete Hartley Transform -- 5.7. Evaluation of the Proposed Techniques -- 5.8. Comparison with Existing Techniques -- 5.9. Statistical Significance | |
| 505 | 8 | |a Chapter Summary -- References -- 6. Content-Based Feature Extraction: Morphological Operators -- 6.1. Prelude -- 6.2. Top-Hat Transform -- 6.3. Code Example (MATLAB) -- 6.4. Coding Exercise -- 6.5. Bottom-Hat Transform -- 6.6. Code Example (MATLAB) -- 6.7. Coding Exercise -- 6.8. Comparison of Proposed Techniques -- 6.9. Comparison with Existing Methods -- 6.10. Statistical Significance -- Chapter Summary -- References -- 7. Content-Based Feature Extraction: Texture Components -- 7.1. Prelude -- 7.2. Feature Extraction by Vector Quantization Codebook Representation Using Linde-Buzo-Grey (LBG) Algorithm -- 7.3. Code Example (MATLAB) -- 7.4. Coding Exercise -- 7.5. Feature Extraction by Gray Level Co-occurrence Matrix (GLCM) -- 7.6. Code Example (MATLAB) -- 7.7. Coding Exercise -- 7.8. Evaluation of Proposed Techniques -- 7.9. Comparison with Existing Methods -- 7.10. Statistical Significance -- Chapter Summary -- References -- 8. Fusion-Based Classification: A Comparison of Early Fusion and Late Fusion Architecture for Content-Based Features -- 8.1. Prelude -- 8.2. Image Preprocessing -- 8.3. Feature Extraction with Image Binarization -- 8.4. Feature Extraction Applying Discrete Cosine Transform (DCT) -- 8.5. Classification Framework -- 8.5.1. Method 1 -- 8.5.2. Method 2 -- 8.6. Classification Results -- Chapter Summary -- References -- 9. Future Directions: A Journey from Handcrafted Techniques to Representation Learning -- 9.1. Prelude -- 9.2. Representation Learning-Based Feature Extraction -- 9.3. Code Example (MATLAB) -- 9.4. Image Color Averaging Techniques -- 9.5. Binarization Techniques -- 9.6. Image Transforms -- 9.7. Morphological Operations -- 9.8. Texture Analysis -- 9.9. Multitechnique Feature Extraction for Decision Fusion-Based Classification -- 9.10. Comparison of Cross Domain Feature Extraction Techniques -- 9.11. Future Work | |
| 505 | 8 | |a References -- 10. WEKA: Beginners' Tutorial -- 10.1. Prelude -- 10.2. Getting Started with WEKA -- References -- Index | |
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| contents | Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Author -- 1. Introduction to Content-Based Image Classification -- 1.1. Prelude -- 1.2. Metrics -- 1.2.1. Precision -- 1.2.2. True Positive (TP) Rate/Recall -- 1.2.3. Misclassification Rate (MR) -- 1.2.4. F1-Score -- 1.2.5. Accuracy -- 1.2.6. False Positive (FP) Rate -- 1.2.7. True Negative (TN) Rate -- 1.2.8. False Negative (FN) Rate -- 1.3. Classifiers -- 1.3.1. KNN Classifier -- 1.3.2. Random Forest Classifier -- 1.3.3. ANN Classifier -- 1.3.4. SVM Classifier -- 1.4. Datasets Used -- 1.4.1. Wang Dataset -- 1.4.2. Caltech Dataset -- 1.4.3. Corel Dataset -- 1.4.4. Oliva Torralba (OT-Scene) Dataset -- 1.5. Organization of the Book -- Chapter Summary -- References -- 2. A Review of Handcrafted Feature Extraction Techniques for Content-Based Image Classification -- 2.1. Prelude -- 2.2. Extraction of Features with Color Contents -- 2.3. Extraction of Features with Image Binarization -- 2.4. Extraction of Features with Image Transforms -- 2.5. Extraction of Features with Morphological Processing -- 2.6. Extraction of Features with Texture Content -- 2.7. Fusion of Features Extracted with Multiple Techniques -- 2.8. Techniques of Classification -- 2.9. Logic-Based Algorithms -- 2.9.1. Decision Trees -- 2.9.2. Learning a Set of Rules -- 2.9.3. Perceptron-Based Techniques -- 2.9.3.1 Single-Layer Perceptrons -- 2.9.3.2 Multilayer Perceptrons -- 2.9.4. Statistical Learning Algorithm -- 2.9.5. Support Vector Machine -- Chapter Summary -- References -- 3. Content-Based Feature Extraction: Color Averaging -- 3.1. Prelude -- 3.2. Block Truncation Coding -- 3.3. Feature Extraction Using Block Truncation Coding with Color Clumps -- 3.4. Code Example (MATLAB) -- 3.5. Coding Exercise 3.6. Feature Extraction Using Sorted Block Truncation Coding for Content-Based Image Classification -- 3.7. Code Example (MATLAB) -- 3.8. Coding Exercise -- 3.9. Comparison of Proposed Techniques -- 3.10. Comparison with Existing Techniques -- 3.11. Statistical Significance -- Chapter Summary -- References -- 4. Content-Based Feature Extraction: Image Binarization -- 4.1. Prelude -- 4.2. Feature Extraction Using Mean Threshold Selection -- 4.2.1. Feature Extraction with Multilevel Mean Threshold Selection -- 4.3. Code Example (MATLAB) -- 4.4. Coding Exercise -- 4.5. Feature Extraction from Significant Bit Planes Using Mean Threshold Selection -- 4.6. Code Example (MATLAB) -- 4.7. Coding Exercise -- 4.8. Feature Extraction from Even and Odd Image Varieties Using Mean Threshold Selection -- 4.9. Code Example (MATLAB) -- 4.10. Coding Exercise -- 4.11. Feature Extraction with Static and Dynamic Ternary Image Maps Using Mean Threshold Selection -- 4.12. Code Example (MATLAB) -- 4.13. Feature Extraction Using Local Threshold Selection -- 4.14. Code Example (MATLAB) -- 4.15. Coding Exercise -- 4.16. Comparing the Discussed Techniques for Performance Evaluation -- 4.17. Comparison with Existing Techniques -- 4.18. Statistical Significance -- Chapter Summary -- References -- 5. Content-Based Feature Extraction: Image Transforms -- 5.1. Prelude -- 5.2. Generating Partial Energy Coefficient from Transformed Images -- 5.3. Code Example (MATLAB) -- 5.4. Coding Exercise -- 5.5. Computational Complexity for the Image Transforms -- 5.6. Feature Extraction with Partial Energy Coefficient -- 5.6.1. Discrete Cosine Transform -- 5.6.2. Walsh Transform -- 5.6.3. Kekre Transform -- 5.6.4. Discrete Sine Transform -- 5.6.5. Discrete Hartley Transform -- 5.7. Evaluation of the Proposed Techniques -- 5.8. Comparison with Existing Techniques -- 5.9. Statistical Significance Chapter Summary -- References -- 6. Content-Based Feature Extraction: Morphological Operators -- 6.1. Prelude -- 6.2. Top-Hat Transform -- 6.3. Code Example (MATLAB) -- 6.4. Coding Exercise -- 6.5. Bottom-Hat Transform -- 6.6. Code Example (MATLAB) -- 6.7. Coding Exercise -- 6.8. Comparison of Proposed Techniques -- 6.9. Comparison with Existing Methods -- 6.10. Statistical Significance -- Chapter Summary -- References -- 7. Content-Based Feature Extraction: Texture Components -- 7.1. Prelude -- 7.2. Feature Extraction by Vector Quantization Codebook Representation Using Linde-Buzo-Grey (LBG) Algorithm -- 7.3. Code Example (MATLAB) -- 7.4. Coding Exercise -- 7.5. Feature Extraction by Gray Level Co-occurrence Matrix (GLCM) -- 7.6. Code Example (MATLAB) -- 7.7. Coding Exercise -- 7.8. Evaluation of Proposed Techniques -- 7.9. Comparison with Existing Methods -- 7.10. Statistical Significance -- Chapter Summary -- References -- 8. Fusion-Based Classification: A Comparison of Early Fusion and Late Fusion Architecture for Content-Based Features -- 8.1. Prelude -- 8.2. Image Preprocessing -- 8.3. Feature Extraction with Image Binarization -- 8.4. Feature Extraction Applying Discrete Cosine Transform (DCT) -- 8.5. Classification Framework -- 8.5.1. Method 1 -- 8.5.2. Method 2 -- 8.6. Classification Results -- Chapter Summary -- References -- 9. Future Directions: A Journey from Handcrafted Techniques to Representation Learning -- 9.1. Prelude -- 9.2. Representation Learning-Based Feature Extraction -- 9.3. Code Example (MATLAB) -- 9.4. Image Color Averaging Techniques -- 9.5. Binarization Techniques -- 9.6. Image Transforms -- 9.7. Morphological Operations -- 9.8. Texture Analysis -- 9.9. Multitechnique Feature Extraction for Decision Fusion-Based Classification -- 9.10. Comparison of Cross Domain Feature Extraction Techniques -- 9.11. Future Work References -- 10. WEKA: Beginners' Tutorial -- 10.1. Prelude -- 10.2. Getting Started with WEKA -- References -- Index |
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| discipline | Informatik |
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Introduction to Content-Based Image Classification -- 1.1. Prelude -- 1.2. Metrics -- 1.2.1. Precision -- 1.2.2. True Positive (TP) Rate/Recall -- 1.2.3. Misclassification Rate (MR) -- 1.2.4. F1-Score -- 1.2.5. Accuracy -- 1.2.6. False Positive (FP) Rate -- 1.2.7. True Negative (TN) Rate -- 1.2.8. False Negative (FN) Rate -- 1.3. Classifiers -- 1.3.1. KNN Classifier -- 1.3.2. Random Forest Classifier -- 1.3.3. ANN Classifier -- 1.3.4. SVM Classifier -- 1.4. Datasets Used -- 1.4.1. Wang Dataset -- 1.4.2. Caltech Dataset -- 1.4.3. Corel Dataset -- 1.4.4. Oliva Torralba (OT-Scene) Dataset -- 1.5. Organization of the Book -- Chapter Summary -- References -- 2. A Review of Handcrafted Feature Extraction Techniques for Content-Based Image Classification -- 2.1. Prelude -- 2.2. Extraction of Features with Color Contents -- 2.3. Extraction of Features with Image Binarization -- 2.4. Extraction of Features with Image Transforms -- 2.5. 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Comparison of Proposed Techniques -- 3.10. Comparison with Existing Techniques -- 3.11. Statistical Significance -- Chapter Summary -- References -- 4. Content-Based Feature Extraction: Image Binarization -- 4.1. Prelude -- 4.2. Feature Extraction Using Mean Threshold Selection -- 4.2.1. Feature Extraction with Multilevel Mean Threshold Selection -- 4.3. Code Example (MATLAB) -- 4.4. Coding Exercise -- 4.5. Feature Extraction from Significant Bit Planes Using Mean Threshold Selection -- 4.6. Code Example (MATLAB) -- 4.7. Coding Exercise -- 4.8. Feature Extraction from Even and Odd Image Varieties Using Mean Threshold Selection -- 4.9. Code Example (MATLAB) -- 4.10. Coding Exercise -- 4.11. Feature Extraction with Static and Dynamic Ternary Image Maps Using Mean Threshold Selection -- 4.12. Code Example (MATLAB) -- 4.13. Feature Extraction Using Local Threshold Selection -- 4.14. Code Example (MATLAB) -- 4.15. Coding Exercise -- 4.16. 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Code Example (MATLAB) -- 6.4. Coding Exercise -- 6.5. Bottom-Hat Transform -- 6.6. Code Example (MATLAB) -- 6.7. Coding Exercise -- 6.8. Comparison of Proposed Techniques -- 6.9. Comparison with Existing Methods -- 6.10. Statistical Significance -- Chapter Summary -- References -- 7. Content-Based Feature Extraction: Texture Components -- 7.1. Prelude -- 7.2. Feature Extraction by Vector Quantization Codebook Representation Using Linde-Buzo-Grey (LBG) Algorithm -- 7.3. Code Example (MATLAB) -- 7.4. Coding Exercise -- 7.5. Feature Extraction by Gray Level Co-occurrence Matrix (GLCM) -- 7.6. Code Example (MATLAB) -- 7.7. Coding Exercise -- 7.8. Evaluation of Proposed Techniques -- 7.9. Comparison with Existing Methods -- 7.10. Statistical Significance -- Chapter Summary -- References -- 8. Fusion-Based Classification: A Comparison of Early Fusion and Late Fusion Architecture for Content-Based Features -- 8.1. Prelude -- 8.2. Image Preprocessing -- 8.3. Feature Extraction with Image Binarization -- 8.4. Feature Extraction Applying Discrete Cosine Transform (DCT) -- 8.5. Classification Framework -- 8.5.1. Method 1 -- 8.5.2. Method 2 -- 8.6. Classification Results -- Chapter Summary -- References -- 9. Future Directions: A Journey from Handcrafted Techniques to Representation Learning -- 9.1. Prelude -- 9.2. Representation Learning-Based Feature Extraction -- 9.3. Code Example (MATLAB) -- 9.4. Image Color Averaging Techniques -- 9.5. Binarization Techniques -- 9.6. Image Transforms -- 9.7. Morphological Operations -- 9.8. Texture Analysis -- 9.9. Multitechnique Feature Extraction for Decision Fusion-Based Classification -- 9.10. Comparison of Cross Domain Feature Extraction Techniques -- 9.11. Future Work</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">References -- 10. WEKA: Beginners' Tutorial -- 10.1. Prelude -- 10.2. 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| id | DE-604.BV047442161 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:01:24Z |
| indexdate | 2024-07-10T09:12:16Z |
| institution | BVB |
| isbn | 9780429352928 9781000280715 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032844312 |
| oclc_num | 1229166014 |
| open_access_boolean | |
| owner | DE-83 DE-739 |
| owner_facet | DE-83 DE-739 |
| physical | 1 Online-Ressource (xvi, 180 Seiten) |
| psigel | ZDB-7-CSC ZDB-7-TFC ZDB-30-PQE ZDB-7-CSC UPA_PDA_CSC_Kauf2022 |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Press LLC |
| record_format | marc |
| spelling | Das, Rik 1978- Verfasser (DE-588)1212046153 aut Content-based image classification efficient machine learning using robust feature extraction techniques Rik Das Milton CRC Press LLC 2020 ©2021 1 Online-Ressource (xvi, 180 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Author -- 1. Introduction to Content-Based Image Classification -- 1.1. Prelude -- 1.2. Metrics -- 1.2.1. Precision -- 1.2.2. True Positive (TP) Rate/Recall -- 1.2.3. Misclassification Rate (MR) -- 1.2.4. F1-Score -- 1.2.5. Accuracy -- 1.2.6. False Positive (FP) Rate -- 1.2.7. True Negative (TN) Rate -- 1.2.8. False Negative (FN) Rate -- 1.3. Classifiers -- 1.3.1. KNN Classifier -- 1.3.2. Random Forest Classifier -- 1.3.3. ANN Classifier -- 1.3.4. SVM Classifier -- 1.4. Datasets Used -- 1.4.1. Wang Dataset -- 1.4.2. Caltech Dataset -- 1.4.3. Corel Dataset -- 1.4.4. Oliva Torralba (OT-Scene) Dataset -- 1.5. Organization of the Book -- Chapter Summary -- References -- 2. A Review of Handcrafted Feature Extraction Techniques for Content-Based Image Classification -- 2.1. Prelude -- 2.2. Extraction of Features with Color Contents -- 2.3. Extraction of Features with Image Binarization -- 2.4. Extraction of Features with Image Transforms -- 2.5. Extraction of Features with Morphological Processing -- 2.6. Extraction of Features with Texture Content -- 2.7. Fusion of Features Extracted with Multiple Techniques -- 2.8. Techniques of Classification -- 2.9. Logic-Based Algorithms -- 2.9.1. Decision Trees -- 2.9.2. Learning a Set of Rules -- 2.9.3. Perceptron-Based Techniques -- 2.9.3.1 Single-Layer Perceptrons -- 2.9.3.2 Multilayer Perceptrons -- 2.9.4. Statistical Learning Algorithm -- 2.9.5. Support Vector Machine -- Chapter Summary -- References -- 3. Content-Based Feature Extraction: Color Averaging -- 3.1. Prelude -- 3.2. Block Truncation Coding -- 3.3. Feature Extraction Using Block Truncation Coding with Color Clumps -- 3.4. Code Example (MATLAB) -- 3.5. Coding Exercise 3.6. Feature Extraction Using Sorted Block Truncation Coding for Content-Based Image Classification -- 3.7. Code Example (MATLAB) -- 3.8. Coding Exercise -- 3.9. Comparison of Proposed Techniques -- 3.10. Comparison with Existing Techniques -- 3.11. Statistical Significance -- Chapter Summary -- References -- 4. Content-Based Feature Extraction: Image Binarization -- 4.1. Prelude -- 4.2. Feature Extraction Using Mean Threshold Selection -- 4.2.1. Feature Extraction with Multilevel Mean Threshold Selection -- 4.3. Code Example (MATLAB) -- 4.4. Coding Exercise -- 4.5. Feature Extraction from Significant Bit Planes Using Mean Threshold Selection -- 4.6. Code Example (MATLAB) -- 4.7. Coding Exercise -- 4.8. Feature Extraction from Even and Odd Image Varieties Using Mean Threshold Selection -- 4.9. Code Example (MATLAB) -- 4.10. Coding Exercise -- 4.11. Feature Extraction with Static and Dynamic Ternary Image Maps Using Mean Threshold Selection -- 4.12. Code Example (MATLAB) -- 4.13. Feature Extraction Using Local Threshold Selection -- 4.14. Code Example (MATLAB) -- 4.15. Coding Exercise -- 4.16. Comparing the Discussed Techniques for Performance Evaluation -- 4.17. Comparison with Existing Techniques -- 4.18. Statistical Significance -- Chapter Summary -- References -- 5. Content-Based Feature Extraction: Image Transforms -- 5.1. Prelude -- 5.2. Generating Partial Energy Coefficient from Transformed Images -- 5.3. Code Example (MATLAB) -- 5.4. Coding Exercise -- 5.5. Computational Complexity for the Image Transforms -- 5.6. Feature Extraction with Partial Energy Coefficient -- 5.6.1. Discrete Cosine Transform -- 5.6.2. Walsh Transform -- 5.6.3. Kekre Transform -- 5.6.4. Discrete Sine Transform -- 5.6.5. Discrete Hartley Transform -- 5.7. Evaluation of the Proposed Techniques -- 5.8. Comparison with Existing Techniques -- 5.9. Statistical Significance Chapter Summary -- References -- 6. Content-Based Feature Extraction: Morphological Operators -- 6.1. Prelude -- 6.2. Top-Hat Transform -- 6.3. Code Example (MATLAB) -- 6.4. Coding Exercise -- 6.5. Bottom-Hat Transform -- 6.6. Code Example (MATLAB) -- 6.7. Coding Exercise -- 6.8. Comparison of Proposed Techniques -- 6.9. Comparison with Existing Methods -- 6.10. Statistical Significance -- Chapter Summary -- References -- 7. Content-Based Feature Extraction: Texture Components -- 7.1. Prelude -- 7.2. Feature Extraction by Vector Quantization Codebook Representation Using Linde-Buzo-Grey (LBG) Algorithm -- 7.3. Code Example (MATLAB) -- 7.4. Coding Exercise -- 7.5. Feature Extraction by Gray Level Co-occurrence Matrix (GLCM) -- 7.6. Code Example (MATLAB) -- 7.7. Coding Exercise -- 7.8. Evaluation of Proposed Techniques -- 7.9. Comparison with Existing Methods -- 7.10. Statistical Significance -- Chapter Summary -- References -- 8. Fusion-Based Classification: A Comparison of Early Fusion and Late Fusion Architecture for Content-Based Features -- 8.1. Prelude -- 8.2. Image Preprocessing -- 8.3. Feature Extraction with Image Binarization -- 8.4. Feature Extraction Applying Discrete Cosine Transform (DCT) -- 8.5. Classification Framework -- 8.5.1. Method 1 -- 8.5.2. Method 2 -- 8.6. Classification Results -- Chapter Summary -- References -- 9. Future Directions: A Journey from Handcrafted Techniques to Representation Learning -- 9.1. Prelude -- 9.2. Representation Learning-Based Feature Extraction -- 9.3. Code Example (MATLAB) -- 9.4. Image Color Averaging Techniques -- 9.5. Binarization Techniques -- 9.6. Image Transforms -- 9.7. Morphological Operations -- 9.8. Texture Analysis -- 9.9. Multitechnique Feature Extraction for Decision Fusion-Based Classification -- 9.10. Comparison of Cross Domain Feature Extraction Techniques -- 9.11. Future Work References -- 10. WEKA: Beginners' Tutorial -- 10.1. Prelude -- 10.2. Getting Started with WEKA -- References -- Index Optical pattern recognition Klassifikation (DE-588)4030958-7 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 s Klassifikation (DE-588)4030958-7 s Merkmalsextraktion (DE-588)4314440-8 s DE-604 Erscheint auch als Druck-Ausgabe Das, Rik Content-Based Image Classification Milton : CRC Press LLC,c2020 9780367371609 https://doi.org/10.1201/9780429352928 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Das, Rik 1978- Content-based image classification efficient machine learning using robust feature extraction techniques Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Author -- 1. Introduction to Content-Based Image Classification -- 1.1. Prelude -- 1.2. Metrics -- 1.2.1. Precision -- 1.2.2. True Positive (TP) Rate/Recall -- 1.2.3. Misclassification Rate (MR) -- 1.2.4. F1-Score -- 1.2.5. Accuracy -- 1.2.6. False Positive (FP) Rate -- 1.2.7. True Negative (TN) Rate -- 1.2.8. False Negative (FN) Rate -- 1.3. Classifiers -- 1.3.1. KNN Classifier -- 1.3.2. Random Forest Classifier -- 1.3.3. ANN Classifier -- 1.3.4. SVM Classifier -- 1.4. Datasets Used -- 1.4.1. Wang Dataset -- 1.4.2. Caltech Dataset -- 1.4.3. Corel Dataset -- 1.4.4. Oliva Torralba (OT-Scene) Dataset -- 1.5. Organization of the Book -- Chapter Summary -- References -- 2. A Review of Handcrafted Feature Extraction Techniques for Content-Based Image Classification -- 2.1. Prelude -- 2.2. Extraction of Features with Color Contents -- 2.3. Extraction of Features with Image Binarization -- 2.4. Extraction of Features with Image Transforms -- 2.5. Extraction of Features with Morphological Processing -- 2.6. Extraction of Features with Texture Content -- 2.7. Fusion of Features Extracted with Multiple Techniques -- 2.8. Techniques of Classification -- 2.9. Logic-Based Algorithms -- 2.9.1. Decision Trees -- 2.9.2. Learning a Set of Rules -- 2.9.3. Perceptron-Based Techniques -- 2.9.3.1 Single-Layer Perceptrons -- 2.9.3.2 Multilayer Perceptrons -- 2.9.4. Statistical Learning Algorithm -- 2.9.5. Support Vector Machine -- Chapter Summary -- References -- 3. Content-Based Feature Extraction: Color Averaging -- 3.1. Prelude -- 3.2. Block Truncation Coding -- 3.3. Feature Extraction Using Block Truncation Coding with Color Clumps -- 3.4. Code Example (MATLAB) -- 3.5. Coding Exercise 3.6. Feature Extraction Using Sorted Block Truncation Coding for Content-Based Image Classification -- 3.7. Code Example (MATLAB) -- 3.8. Coding Exercise -- 3.9. Comparison of Proposed Techniques -- 3.10. Comparison with Existing Techniques -- 3.11. Statistical Significance -- Chapter Summary -- References -- 4. Content-Based Feature Extraction: Image Binarization -- 4.1. Prelude -- 4.2. Feature Extraction Using Mean Threshold Selection -- 4.2.1. Feature Extraction with Multilevel Mean Threshold Selection -- 4.3. Code Example (MATLAB) -- 4.4. Coding Exercise -- 4.5. Feature Extraction from Significant Bit Planes Using Mean Threshold Selection -- 4.6. Code Example (MATLAB) -- 4.7. Coding Exercise -- 4.8. Feature Extraction from Even and Odd Image Varieties Using Mean Threshold Selection -- 4.9. Code Example (MATLAB) -- 4.10. Coding Exercise -- 4.11. Feature Extraction with Static and Dynamic Ternary Image Maps Using Mean Threshold Selection -- 4.12. Code Example (MATLAB) -- 4.13. Feature Extraction Using Local Threshold Selection -- 4.14. Code Example (MATLAB) -- 4.15. Coding Exercise -- 4.16. Comparing the Discussed Techniques for Performance Evaluation -- 4.17. Comparison with Existing Techniques -- 4.18. Statistical Significance -- Chapter Summary -- References -- 5. Content-Based Feature Extraction: Image Transforms -- 5.1. Prelude -- 5.2. Generating Partial Energy Coefficient from Transformed Images -- 5.3. Code Example (MATLAB) -- 5.4. Coding Exercise -- 5.5. Computational Complexity for the Image Transforms -- 5.6. Feature Extraction with Partial Energy Coefficient -- 5.6.1. Discrete Cosine Transform -- 5.6.2. Walsh Transform -- 5.6.3. Kekre Transform -- 5.6.4. Discrete Sine Transform -- 5.6.5. Discrete Hartley Transform -- 5.7. Evaluation of the Proposed Techniques -- 5.8. Comparison with Existing Techniques -- 5.9. Statistical Significance Chapter Summary -- References -- 6. Content-Based Feature Extraction: Morphological Operators -- 6.1. Prelude -- 6.2. Top-Hat Transform -- 6.3. Code Example (MATLAB) -- 6.4. Coding Exercise -- 6.5. Bottom-Hat Transform -- 6.6. Code Example (MATLAB) -- 6.7. Coding Exercise -- 6.8. Comparison of Proposed Techniques -- 6.9. Comparison with Existing Methods -- 6.10. Statistical Significance -- Chapter Summary -- References -- 7. Content-Based Feature Extraction: Texture Components -- 7.1. Prelude -- 7.2. Feature Extraction by Vector Quantization Codebook Representation Using Linde-Buzo-Grey (LBG) Algorithm -- 7.3. Code Example (MATLAB) -- 7.4. Coding Exercise -- 7.5. Feature Extraction by Gray Level Co-occurrence Matrix (GLCM) -- 7.6. Code Example (MATLAB) -- 7.7. Coding Exercise -- 7.8. Evaluation of Proposed Techniques -- 7.9. Comparison with Existing Methods -- 7.10. Statistical Significance -- Chapter Summary -- References -- 8. Fusion-Based Classification: A Comparison of Early Fusion and Late Fusion Architecture for Content-Based Features -- 8.1. Prelude -- 8.2. Image Preprocessing -- 8.3. Feature Extraction with Image Binarization -- 8.4. Feature Extraction Applying Discrete Cosine Transform (DCT) -- 8.5. Classification Framework -- 8.5.1. Method 1 -- 8.5.2. Method 2 -- 8.6. Classification Results -- Chapter Summary -- References -- 9. Future Directions: A Journey from Handcrafted Techniques to Representation Learning -- 9.1. Prelude -- 9.2. Representation Learning-Based Feature Extraction -- 9.3. Code Example (MATLAB) -- 9.4. Image Color Averaging Techniques -- 9.5. Binarization Techniques -- 9.6. Image Transforms -- 9.7. Morphological Operations -- 9.8. Texture Analysis -- 9.9. Multitechnique Feature Extraction for Decision Fusion-Based Classification -- 9.10. Comparison of Cross Domain Feature Extraction Techniques -- 9.11. Future Work References -- 10. WEKA: Beginners' Tutorial -- 10.1. Prelude -- 10.2. Getting Started with WEKA -- References -- Index Optical pattern recognition Klassifikation (DE-588)4030958-7 gnd Bildverarbeitung (DE-588)4006684-8 gnd Merkmalsextraktion (DE-588)4314440-8 gnd |
| subject_GND | (DE-588)4030958-7 (DE-588)4006684-8 (DE-588)4314440-8 |
| title | Content-based image classification efficient machine learning using robust feature extraction techniques |
| title_auth | Content-based image classification efficient machine learning using robust feature extraction techniques |
| title_exact_search | Content-based image classification efficient machine learning using robust feature extraction techniques |
| title_exact_search_txtP | Content-based image classification efficient machine learning using robust feature extraction techniques |
| title_full | Content-based image classification efficient machine learning using robust feature extraction techniques Rik Das |
| title_fullStr | Content-based image classification efficient machine learning using robust feature extraction techniques Rik Das |
| title_full_unstemmed | Content-based image classification efficient machine learning using robust feature extraction techniques Rik Das |
| title_short | Content-based image classification |
| title_sort | content based image classification efficient machine learning using robust feature extraction techniques |
| title_sub | efficient machine learning using robust feature extraction techniques |
| topic | Optical pattern recognition Klassifikation (DE-588)4030958-7 gnd Bildverarbeitung (DE-588)4006684-8 gnd Merkmalsextraktion (DE-588)4314440-8 gnd |
| topic_facet | Optical pattern recognition Klassifikation Bildverarbeitung Merkmalsextraktion |
| url | https://doi.org/10.1201/9780429352928 |
| work_keys_str_mv | AT dasrik contentbasedimageclassificationefficientmachinelearningusingrobustfeatureextractiontechniques |