A Novel Shape Classification Approach Based on Branch Length Similarity Entropy
This study presents a novel feature vector for shape clustering based on the Branch Length Similarity (BLS) entropy profile, which is invariant to translation, rotation, and scaling, enhancing its effectiveness for shape analysis. The methodology consists of two steps: the t-distributed Stochastic N...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11045929/ |
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| author | Sang-Hee Lee Cheol-Min Park |
| author_facet | Sang-Hee Lee Cheol-Min Park |
| author_sort | Sang-Hee Lee |
| collection | DOAJ |
| description | This study presents a novel feature vector for shape clustering based on the Branch Length Similarity (BLS) entropy profile, which is invariant to translation, rotation, and scaling, enhancing its effectiveness for shape analysis. The methodology consists of two steps: the t-distributed Stochastic Neighbor Embedding (t-SNE) technique, which projects feature vectors into a two-dimensional space, and the k-means algorithm, which groups these points into clusters. We applied this approach to three datasets—MPEG-7, Swedish Leaf, and Heptagon—and evaluated its performance. The results revealed significant geometric similarities within clusters, demonstrating the method’s efficacy. Logistic curve modeling was employed to determine the optimal number of clusters, with the Swedish Leaf dataset achieving the highest clustering score (0.885), followed by scores of 0.76 for the MPEG-7 dataset and 0.731 for the Heptagon dataset. The comparative analysis demonstrated that the proposed method outperforms conventional approaches based on Fourier descriptors or Zernike moments, providing a robust and adaptable solution for shape classification across diverse datasets. |
| format | Article |
| id | doaj-art-707f12c666c94ae89fc11eafdad99dc3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-707f12c666c94ae89fc11eafdad99dc32025-08-20T03:27:14ZengIEEEIEEE Access2169-35362025-01-011310818010819110.1109/ACCESS.2025.358190111045929A Novel Shape Classification Approach Based on Branch Length Similarity EntropySang-Hee Lee0https://orcid.org/0000-0003-2708-071XCheol-Min Park1https://orcid.org/0000-0001-5257-7723Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of KoreaDivision of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of KoreaThis study presents a novel feature vector for shape clustering based on the Branch Length Similarity (BLS) entropy profile, which is invariant to translation, rotation, and scaling, enhancing its effectiveness for shape analysis. The methodology consists of two steps: the t-distributed Stochastic Neighbor Embedding (t-SNE) technique, which projects feature vectors into a two-dimensional space, and the k-means algorithm, which groups these points into clusters. We applied this approach to three datasets—MPEG-7, Swedish Leaf, and Heptagon—and evaluated its performance. The results revealed significant geometric similarities within clusters, demonstrating the method’s efficacy. Logistic curve modeling was employed to determine the optimal number of clusters, with the Swedish Leaf dataset achieving the highest clustering score (0.885), followed by scores of 0.76 for the MPEG-7 dataset and 0.731 for the Heptagon dataset. The comparative analysis demonstrated that the proposed method outperforms conventional approaches based on Fourier descriptors or Zernike moments, providing a robust and adaptable solution for shape classification across diverse datasets.https://ieeexplore.ieee.org/document/11045929/Classificationdata structurediscrete transformsentropyshape detection |
| spellingShingle | Sang-Hee Lee Cheol-Min Park A Novel Shape Classification Approach Based on Branch Length Similarity Entropy IEEE Access Classification data structure discrete transforms entropy shape detection |
| title | A Novel Shape Classification Approach Based on Branch Length Similarity Entropy |
| title_full | A Novel Shape Classification Approach Based on Branch Length Similarity Entropy |
| title_fullStr | A Novel Shape Classification Approach Based on Branch Length Similarity Entropy |
| title_full_unstemmed | A Novel Shape Classification Approach Based on Branch Length Similarity Entropy |
| title_short | A Novel Shape Classification Approach Based on Branch Length Similarity Entropy |
| title_sort | novel shape classification approach based on branch length similarity entropy |
| topic | Classification data structure discrete transforms entropy shape detection |
| url | https://ieeexplore.ieee.org/document/11045929/ |
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