{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:41:04Z","timestamp":1760035264497,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007166","name":"Natural Science Foundation of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["24KJB520005"],"award-info":[{"award-number":["24KJB520005"]}],"id":[{"id":"10.13039\/501100007166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Leaf shape is a crucial visual cue for plant recognition. However, distinguishing among plants with high inter-class shape similarity remains a significant challenge, especially among cultivars within the same species where shape differences can be extremely subtle. To address this issue, we propose a novel shape representation and an advanced heterogeneous fusion framework for accurate leaf image retrieval. Specifically, based on the local polar coordinate system, multiscale analysis, and statistical histograms, we first propose local polar coordinate feature representation (LPCFR), which captures spatial distribution from two orthogonal directions while encoding local curvature characteristics. Next, we present heterogeneous feature fusion with exponential weighting and Ranking (HFER), which enhances the compatibility and robustness of fused features by applying exponential weighted normalization and ranking-based encoding within neighborhood distance measures. Extensive experiments on both species-level and cultivar-level leaf datasets demonstrate that the proposed representation effectively captures shape features, and the fusion framework successfully integrates heterogeneous features, outperforming state-of-the-art (SOTA) methods.<\/jats:p>","DOI":"10.3390\/sym17071049","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:07:17Z","timestamp":1751515637000},"page":"1049","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Local Polar Coordinate Feature Representation and Heterogeneous Fusion Framework for Accurate Leaf Image Retrieval"],"prefix":"10.3390","volume":"17","author":[{"given":"Mengjie","family":"Ye","sequence":"first","affiliation":[{"name":"School of Information Engineering, Jiangsu Open University, Nanjng 210019, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8495-6648","authenticated-orcid":false,"given":"Yong","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangsu Open University, Nanjng 210019, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2846-7038","authenticated-orcid":false,"given":"Yongqi","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangsu Open University, Nanjng 210019, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6027-1347","authenticated-orcid":false,"given":"De","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangsu Open University, Nanjng 210019, China"}]},{"given":"Ge","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jiangsu Open University, Nanjng 210019, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1109\/TCSVT.2004.826776","article-title":"A multiscale representation method for nonrigid shapes with a single closed contour","volume":"14","author":"Adamek","year":"2004","journal-title":"IEEE Trans. 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