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缩小语义鸿沟在基于内容的图像检索

文摘

图像内容在网络上呈几何倍数增长。因此,有必要对图像检索系统。从历史上看,有两种方法,基于文本和基于内容的。在基于文本的方法中,查询系统检索图像,人工注释使用关键字。这种方法可以是有问题的:它是劳动密集型的,也许根据观察者的主观偏见。基于内容的图像检索(CBIR)在大型数据库搜索和检索数字图像分析derived-image特性。CBIR系统通常使用的特征颜色,纹理,形状及其组合特征的定义。相似的措施起源于前通常使用基于文本的时代。然而,CBIR斗争缩小语义鸿沟,定义为高层CBIR的复杂性和人类感知与底层实现的特性和技术。本文综述了CBIR在广泛的上下文。 Newer approaches is feature generation and similarity measures are detailed with representative studies addressing their efficacy. Color-texture moments, columns-of-interest, harmony-symmetry-geometry, SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) are presented as alternative feature generation modalities. Graph matching, Earth Mover’s Distance, and relevance feedback are discussed with the realm of similarity. We conclude that while CBIR is evolving and continues to slowly close the semantic gap, addressing the complexity of human perception remains a challenge.

保罗·c·郭

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