{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:31:17Z","timestamp":1774927877808,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Soft Science Research Program \u201cResearch on the Mechanism of Data Elements Empowering Rural Revitalization in the Context of Digital Villages\u201d","award":["2023C35083"],"award-info":[{"award-number":["2023C35083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Image matching is a fundamental problem in computer vision, serving as a core component in tasks such as visual localization, structure from motion, and SLAM. While recent advances using convolutional neural networks and transformer have achieved impressive accuracy, their substantial computational demands hinder practical deployment on resource-constrained devices, such as mobile and embedded platforms. To address this challenge, we propose LIM, a lightweight image local feature matching network designed for computationally constrained embedded systems. LIM integrates efficient feature extraction and matching modules that significantly reduce model complexity while maintaining competitive performance. Our design emphasizes robustness to extreme viewpoint and rotational variations, making it suitable for real-world deployment scenarios. Extensive experiments on multiple benchmarks demonstrate that LIM achieves a favorable trade-off between speed and accuracy, running more than 3\u00d7 faster than existing deep matching methods, while preserving high-quality matching results. These characteristics position LIM as an effective solution for real-time applications in power-limited environments.<\/jats:p>","DOI":"10.3390\/jimaging11050164","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T06:10:46Z","timestamp":1747721446000},"page":"164","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LIM: Lightweight Image Local Feature Matching"],"prefix":"10.3390","volume":"11","author":[{"given":"Shanquan","family":"Ying","sequence":"first","affiliation":[{"name":"College of Science and Technology, Ningbo University, Ningbo 315212, China"}]},{"given":"Jianfeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Science and Technology, Ningbo University, Ningbo 315212, China"}]},{"given":"Guannan","family":"Li","sequence":"additional","affiliation":[{"name":"Huzhou Institute of Zhejiang University, Huzhou 313000, China"}]},{"given":"Junjie","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Science and Technology, Ningbo University, Ningbo 315212, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Edstedt, J., Athanasiadis, I., Wadenb\u00e4ck, M., and Felsberg, M. (2023, January 17\u201324). DKM: Dense kernelized feature matching for geometry estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01704"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Edstedt, J., Sun, Q., B\u00f6kman, G., Wadenb\u00e4ck, M., and Felsberg, M. (2024, January 16\u201322). RoMa: Robust dense feature matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01871"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sun, J., Shen, Z., Wang, Y., Bao, H., and Zhou, X. (2021, January 20\u201325). LoFTR: Detector-free local feature matching with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10247","DOI":"10.1109\/TPAMI.2023.3249225","article-title":"Pdc-net+: Enhanced probabilistic dense correspondence network","volume":"45","author":"Truong","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Low","year":"2004","journal-title":"J. Comput. Vis."},{"key":"ref_6","unstructured":"Viswanathan, D.G. (2009, January 6\u20138). Features from accelerated segment test (fast). Proceedings of the 10th Workshop on Image Analysis for Multimedia Interactive Services, London, UK."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006, January 7\u201313). Surf: Speeded up robust features. Proceedings of the Computer Vision\u2013ECCV 2006: 9th European Conference on Computer Vision, Proceedings, Part I 9, Graz, Austria.","DOI":"10.1007\/11744023_32"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yi, K.M., Trulls, E., Lepetit, V., and Fua, P. (2016, January 11\u201314). Lift: Learned invariant feature transform. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Proceedings, Part VI 14, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46466-4_28"},{"key":"ref_10","first-page":"14254","article-title":"DISK: Learning local features with policy gradient","volume":"33","author":"Tyszkiewicz","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201323). Superpoint: Self-supervised interest point detection and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gleize, P., Wang, W., and Feiszli, M. (2023, January 2\u20133). Silk: Simple learned keypoints. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.02056"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.1109\/TMM.2022.3155927","article-title":"ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction","volume":"25","author":"Zhao","year":"2023","journal-title":"IEEE Multimed."},{"key":"ref_14","first-page":"1","article-title":"Aliked: A lighter keypoint and descriptor extraction network via deformable transformation","volume":"72","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Edstedt, J., B\u00f6kman, G., Wadenb\u00e4ck, M., and Felsberg, M. (2024, January 18\u201321). DeDoDe: Detect, don\u2019t describe\u2014Describe, don\u2019t detect for local feature matching. Proceedings of the 2024 International Conference on 3D Vision (3DV), Davos, Switzerland.","DOI":"10.1109\/3DV62453.2024.00035"},{"key":"ref_16","first-page":"405","article-title":"The interpretation of structure from motion","volume":"203","author":"Ullman","year":"1979","journal-title":"Proc. R. Soc. Lond. Ser. Biol. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Eppstein, D., and Erickson, J. (1998, January 7\u201310). Raising roofs, crashing cycles, and playing pool: Applications of a data structure for finding pairwise interactions. Proceedings of the Fourteenth Annual Symposium on Computational Geometry, Minneapolis, MN, USA.","DOI":"10.1145\/276884.276891"},{"key":"ref_18","first-page":"219","article-title":"Programme de classification hi\u00e9rarchique par l\u2019algorithme de la recherche en cha\u00eene des voisins r\u00e9ciproques","volume":"7","author":"Juan","year":"1982","journal-title":"Les Cah. L\u2019Analyse Des Donn\u00e9Es"},{"key":"ref_19","unstructured":"Moore, A.W. (1991). An Introductory Tutorial on Kd-Trees, Computer Laboratory, University of Cambridge. Technical Report."},{"key":"ref_20","unstructured":"Gionis, A., Indyk, P., and Motwani, R. (1999, January 7\u201310). Similarity search in high dimensions via hashing. Proceedings of the Vldb, Scotland, UK."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sarlin, P.-E., DeTone, D., Malisiewicz, T., and Rabinovich, A. (2020, January 13\u201319). Superglue: Learning feature matching with graph neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lindenberger, P., Sarlin, P.-E., and Pollefeys, M. (2023, January 2\u20133). Lightglue: Local feature matching at light speed. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01616"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y., He, X., Peng, S., Tan, D., and Zhou, X. (2024, January 16\u201322). Efficient LoFTR: Semi-dense local feature matching with sparse-like speed. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02047"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jiang, H., Karpur, A., Cao, B., Huang, Q., and Araujo, A. (2024, January 16\u201322). Omniglue: Generalizable feature matching with foundation model guidance. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01878"},{"key":"ref_25","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., and El-Nouby, A. (2023). Dinov2: Learning robust visual features without supervision. arXiv."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","unstructured":"Sifre, L., and Mallat, S. (2014). Rigid-motion scattering for texture classification. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kanakis, M., Maurer, S., Spallanzani, M., Chhatkuli, A., and Van Gool, L. (2023, January 17\u201324). Zippypoint: Fast interest point detection, description, and matching through mixed precision discretization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00651"},{"key":"ref_31","unstructured":"Cohen, T.S., and Welling, M. (2016, January 20\u201322). Group equivariant convolutional networks. Proceedings of the 33rd International Conference on International Conference on Machine Learning\u2014Volume 48, New York, NY, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Z., and Snavely, N. (2018, January 18\u201323). Megadepth: Learning single-view depth prediction from internet photos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00218"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Balntas, V., Lenc, K., Vedaldi, A., and Mikolajczyk, K. (2017, January 21\u201326). HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.410"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sattler, T., Maddern, W., Toft, C., Torii, A., Hammarstrand, L., Stenborg, E., Safari, D., Okutomi, M., Pollefeys, M., and Sivic, J. (2018, January 18\u201323). Benchmarking 6dof outdoor visual localization in changing conditions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00897"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Barath, D., Noskova, J., Ivashechkin, M., and Matas, J. (2020, January 13\u201319). MAGSAC++, a fast, reliable and accurate robust estimator. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00138"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/5\/164\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:35:33Z","timestamp":1760031333000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/5\/164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,20]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["jimaging11050164"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11050164","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,20]]}}}