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The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.<\/jats:p>","DOI":"10.3233\/jifs-179970","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T10:18:30Z","timestamp":1594376310000},"page":"4847-4858","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection"],"prefix":"10.1177","volume":"39","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[{"name":"The School of Physical Education, Shandong University, Jinan, China"}]},{"given":"Jinhai","family":"Sun","sequence":"additional","affiliation":[{"name":"The School of Physical Education, Shandong University, Jinan, China"}]},{"given":"Tuojian","family":"Li","sequence":"additional","affiliation":[{"name":"The School of Physical Education, Shandong University, Jinan, China"}]}],"member":"179","published-online":{"date-parts":[[2020,7,9]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition","volume":"2016","author":"Lotz J.M.","unstructured":"LotzJ.M., HoffmannF., LotzJ., et al., Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition, Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 2016, S1570963916301807.","journal-title":"Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics"},{"key":"e_1_3_2_3_2","article-title":"High-quality point sampling for B-spline fitting of parametric curves with feature recognition","volume":"2018","author":"Lu L.","unstructured":"LuL. and ZhaoS., High-quality point sampling for B-spline fitting of parametric curves with feature recognition, Journal of Computational and Applied Mathematics 2018, S0377042718301766.","journal-title":"Journal of Computational and Applied Mathematics"},{"key":"e_1_3_2_4_2","unstructured":"GiampaoloC. and MattiaM. 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