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State of the art of content-based image classification

机译：基于内容的图像分类的最新技术

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Explosive growth of digital technologies has spawned a plethora of images available online. Therefore, content-based image classification has been the subject of many research works in recent years. This paper reviewed some of the most commonly used image classification approaches. Most of the existing approaches used low-level features and intermediate semantic modelling. Image classification using low-level features used colour, texture and shape features directly from the image in combination with learning methods to classify images into several semantic classes (i.e. indoor, outdoor, city, landscape, sunset, forest, etc.). Alternatively, intermediate semantic modelling assigned semantic concepts (i.e. sky, people, grass, etc.) to the image content and the image was classified based on these semantic concepts. Low-level strategies were often used when a small number of categories have to be recognized, as well as when the categories were easily separated. Nevertheless, as the number and ambiguity of the categories increase it was clear that approaches using intermediate semantic concepts were more appropriate.
机译：数字技术的爆炸性增长催生了大量在线可用的图像。因此，基于内容的图像分类已成为近年来许多研究工作的主题。本文回顾了一些最常用的图像分类方法。现有的大多数方法都使用低级功能和中间语义建模。使用低级特征进行图像分类的方法是直接从图像中使用颜色，纹理和形状特征，并结合学习方法将图像分为几种语义类别（即室内，室外，城市，风景，日落，森林等）。可替代地，中间语义建模将语义概念（即，天空，人，草等）分配给图像内容，并且基于这些语义概念对图像进行分类。当必须识别少量类别以及容易区分类别时，通常使用低级策略。然而，随着类别的数量和歧义性的增加，很明显，使用中间语义概念的方法更为合适。

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