Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme
Image classification involves the act of classifying images according to their extracted and selected features. Some of the main problems of image classification are the poor features that does not precisely represent an image, and the large dimensionality of data input passed to classifiers. To ove...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41310/1/41310.pdf |
| _version_ | 1825949761246593024 |
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| author | Ashour, Mohammed Waleed Khalid, Fatimah Al-Obaydee, Mohammed |
| author_facet | Ashour, Mohammed Waleed Khalid, Fatimah Al-Obaydee, Mohammed |
| author_sort | Ashour, Mohammed Waleed |
| collection | UPM |
| description | Image classification involves the act of classifying images according to their extracted and selected features. Some of the main problems of image classification are the poor features that does not precisely represent an image, and the large dimensionality of data input passed to classifiers. To overcome these problems, an efficient feature extraction and selection technique is required which extracts and reduces the number of selected features and thus improves the classification accuracy. In this paper, feature extractions scheme followed by features dimensionality reduction technique is presented for image classification. The proposed methodology focuses mainly on three main stages for an input image, firstly extracting features by commonly used features extraction methods such as edge detection, and histogram. Secondly reducing the numbers of extracted features vector using the concept of Principal Component Analysis (PCA) for features selection and vectors dimensionality reduction. Finally, the feature vectors selected by the proposed technique are then input to a supervised Artificial Neural Network (ANN) classifier. Experiments are conducted on a dataset of 72 multi-class engineering surface textures produced by six different machining processes. The classification accuracy rate is calculated after testing 36 samples from our dataset. The experimental results show that the proposed algorithm is superior to some recent algorithms presented in the literature in many respects. |
| first_indexed | 2024-03-06T08:49:32Z |
| format | Conference or Workshop Item |
| id | upm.eprints-41310 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:49:32Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | upm.eprints-413102016-02-04T00:44:01Z http://psasir.upm.edu.my/id/eprint/41310/ Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme Ashour, Mohammed Waleed Khalid, Fatimah Al-Obaydee, Mohammed Image classification involves the act of classifying images according to their extracted and selected features. Some of the main problems of image classification are the poor features that does not precisely represent an image, and the large dimensionality of data input passed to classifiers. To overcome these problems, an efficient feature extraction and selection technique is required which extracts and reduces the number of selected features and thus improves the classification accuracy. In this paper, feature extractions scheme followed by features dimensionality reduction technique is presented for image classification. The proposed methodology focuses mainly on three main stages for an input image, firstly extracting features by commonly used features extraction methods such as edge detection, and histogram. Secondly reducing the numbers of extracted features vector using the concept of Principal Component Analysis (PCA) for features selection and vectors dimensionality reduction. Finally, the feature vectors selected by the proposed technique are then input to a supervised Artificial Neural Network (ANN) classifier. Experiments are conducted on a dataset of 72 multi-class engineering surface textures produced by six different machining processes. The classification accuracy rate is calculated after testing 36 samples from our dataset. The experimental results show that the proposed algorithm is superior to some recent algorithms presented in the literature in many respects. 2013 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/41310/1/41310.pdf Ashour, Mohammed Waleed and Khalid, Fatimah and Al-Obaydee, Mohammed (2013) Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme. In: International Conference on Artificial Intelligence in Computer Science and ICT (AICS 2013), 25-26 Nov. 2013, Langkawi, Malaysia. (pp. 71-80). http://worldconferences.net/proceedings/aics2013/toc/papers_aics2013/A035%20-%20MOHAMMED%20W.%20ASHOUR%20-%20SUPERVISED%20ANN%20CLASSIFICATION.pdf |
| spellingShingle | Ashour, Mohammed Waleed Khalid, Fatimah Al-Obaydee, Mohammed Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme |
| title | Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme |
| title_full | Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme |
| title_fullStr | Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme |
| title_full_unstemmed | Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme |
| title_short | Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme |
| title_sort | supervised ann classification for engineering machined textures based on enhanced features extraction and reduction scheme |
| url | http://psasir.upm.edu.my/id/eprint/41310/1/41310.pdf |
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