Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing
Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using bo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/15/3130 |
| _version_ | 1819131175491338240 |
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| author | Carlos F. Navarro Claudio A. Perez |
| author_facet | Carlos F. Navarro Claudio A. Perez |
| author_sort | Carlos F. Navarro |
| collection | DOAJ |
| description | Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color−texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases. |
| first_indexed | 2024-12-22T09:11:20Z |
| format | Article |
| id | doaj.art-d751ef10d1344b6583fa9f8553ed9231 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-22T09:11:20Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d751ef10d1344b6583fa9f8553ed92312022-12-21T18:31:26ZengMDPI AGApplied Sciences2076-34172019-08-01915313010.3390/app9153130app9153130Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-ProcessingCarlos F. Navarro0Claudio A. Perez1Image Processing Laboratory, Electrical Engineering Department and Advanced Mining Technology Center, Universidad de Chile, Santiago 8370451, ChileImage Processing Laboratory, Electrical Engineering Department and Advanced Mining Technology Center, Universidad de Chile, Santiago 8370451, ChileMany applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color−texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.https://www.mdpi.com/2076-3417/9/15/3130colored texture pattern classificationglobal–local texture classificationcolor–texture featurescolor–texture feature extractionbagging post-processingBQMP and Haralick global–local feature integration |
| spellingShingle | Carlos F. Navarro Claudio A. Perez Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing Applied Sciences colored texture pattern classification global–local texture classification color–texture features color–texture feature extraction bagging post-processing BQMP and Haralick global–local feature integration |
| title | Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing |
| title_full | Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing |
| title_fullStr | Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing |
| title_full_unstemmed | Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing |
| title_short | Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing |
| title_sort | color texture pattern classification using global local feature extraction an svm classifier with bagging ensemble post processing |
| topic | colored texture pattern classification global–local texture classification color–texture features color–texture feature extraction bagging post-processing BQMP and Haralick global–local feature integration |
| url | https://www.mdpi.com/2076-3417/9/15/3130 |
| work_keys_str_mv | AT carlosfnavarro colortexturepatternclassificationusinggloballocalfeatureextractionansvmclassifierwithbaggingensemblepostprocessing AT claudioaperez colortexturepatternclassificationusinggloballocalfeatureextractionansvmclassifierwithbaggingensemblepostprocessing |