Texture feature extraction using the Sequency-ordered Complex Hadamard Transform
In the field of texture classification, signal processing is one of the most common methods used for texture feature extraction. In this paper, we compare the texture classification performance of the sequency-ordered complex Hadamard transform (SCHT) and its real and conjugate symmetric version (Re...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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2011
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| Online Access: | http://hdl.handle.net/10356/45701 |
| _version_ | 1826114234246758400 |
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| author | Lee, Sin Yi. |
| author2 | Ng Boon Poh |
| author_facet | Ng Boon Poh Lee, Sin Yi. |
| author_sort | Lee, Sin Yi. |
| collection | NTU |
| description | In the field of texture classification, signal processing is one of the most common methods used for texture feature extraction. In this paper, we compare the texture classification performance of the sequency-ordered complex Hadamard transform (SCHT) and its real and conjugate symmetric version (Real-CSSCHT) with other existing transforms such as discrete cosine transform (DCT), Walsh Hadamard transform (WHT) and the parametric Slant Hadamard transform (parametric SHT). In our experiments, feature vectors of different texture images were fed into the K-Nearest Neighbor (KNN) classifier to be trained and classified. Classification performance of each transform was analyzed based on factors such as classification accuracy and computational cost. |
| first_indexed | 2024-10-01T03:35:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/45701 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:35:50Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | ntu-10356/457012023-07-07T16:13:36Z Texture feature extraction using the Sequency-ordered Complex Hadamard Transform Lee, Sin Yi. Ng Boon Poh School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing In the field of texture classification, signal processing is one of the most common methods used for texture feature extraction. In this paper, we compare the texture classification performance of the sequency-ordered complex Hadamard transform (SCHT) and its real and conjugate symmetric version (Real-CSSCHT) with other existing transforms such as discrete cosine transform (DCT), Walsh Hadamard transform (WHT) and the parametric Slant Hadamard transform (parametric SHT). In our experiments, feature vectors of different texture images were fed into the K-Nearest Neighbor (KNN) classifier to be trained and classified. Classification performance of each transform was analyzed based on factors such as classification accuracy and computational cost. Bachelor of Engineering 2011-06-16T04:29:22Z 2011-06-16T04:29:22Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/45701 en Nanyang Technological University 67 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Lee, Sin Yi. Texture feature extraction using the Sequency-ordered Complex Hadamard Transform |
| title | Texture feature extraction using the Sequency-ordered Complex Hadamard Transform |
| title_full | Texture feature extraction using the Sequency-ordered Complex Hadamard Transform |
| title_fullStr | Texture feature extraction using the Sequency-ordered Complex Hadamard Transform |
| title_full_unstemmed | Texture feature extraction using the Sequency-ordered Complex Hadamard Transform |
| title_short | Texture feature extraction using the Sequency-ordered Complex Hadamard Transform |
| title_sort | texture feature extraction using the sequency ordered complex hadamard transform |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | http://hdl.handle.net/10356/45701 |
| work_keys_str_mv | AT leesinyi texturefeatureextractionusingthesequencyorderedcomplexhadamardtransform |