基于Hsv颜色空间和特征提取的食品分类Knn算法

Anggun Pambudi, M. A. Rofiq, M. Anwar Sadat, M. Arief Soeleman, Ricardus Anggi P
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引用次数: 0

摘要

印尼的营养是世界上最丰富的烹饪传统之一,其特点是其坚实的味道。这种营养在某些烹饪方法中需要额外的佐料。无论如何,大多数食物照片都是在社交媒体上传播的,因为它们包含的营养成分很少,难以预测,也很难区分。事实上,一些需要在搜索引擎中查找营养的客户仍然使用内容形式的查询。本文认为,提出了结合HSV色彩空间的KNN算法,包括基于直方图的表面特征提取、空隙度提取、GLCM提取等,将营养物质束在图片的形式内。在KNN上使用(k)尊重的测试中,在k =1和k =3的值下,最优秀的高光来自于Lacunarity高光、基于直方图的表面高光与颜色空间HSV的组合,以及k =1和k =3的尊重F1-Score执行尊重。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knn Algorithm For Foodstuff Classification Using Hsv Color Space And Feature Extraction
Indonesian nourishment is one of the wealthiest culinary conventions in the world, characterized by its solid taste. This sort of nourishment requires extra fixings in certain sorts of cooking. In any case, most of the foodstuff photographs were disseminated on social media as they contained few nourishment fixings that are unpredictable and troublesome to distinguish. In truth, several clients who need to discover approximately the fixing of nourishment in look engines still utilize inquiries within the shape of content. This thinks, proposes the KNN algorithm combined with HSV color space and includes extraction, such as histogram-based surface features, lacunarity, and GLCM to bunch the nourishment within the form of pictures. In testing utilizing the (k) esteem on KNN, the most excellent highlight comes about are gotten that deliver a tall level of precision, review, and exactness of 100% at values K=1 and K=3 comes about from the combination of Lacunarity highlights, histogram-based surface highlights with color space HSV, and from the esteem of K=1 and K=3 F1-Score execution esteem.
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