在当前大数据时代,图像由于具有丰富的语义而成为大众获取相关信息的重要来源.基于深度模型的图像语义分析是一种通过深度模型将图像内容转换成可直观理解的语义知识的技术,受到了国内外研究者的广泛关注.该技术根据生成目标语义层次的差异,可分为单类别、多标签和语句3类.首先介绍了以上3类方法对应的深度模型的结构特点,并从技术的演化趋势角度对比分析了3类方法的技术特点和发展现状;然后重点对图像语句转换方法的发展现状、应用场景与性能要求的差异进行了论述,同时对图像语句转换方法的步骤进行分解和论述,从学术界和产业界两方面进行了详细的对比分析,指出了二者的不同研究侧重点与对应的发展现状;最后对具有深度模型的图像语句转换方法进行了总结和展望,指明了该方法当前存在的问题与发展趋势.%In the context of big data,the number of images increases rapidly,and knowledge acquisition is of great sig-nificance to the use and analysis of images.Image semantic analysis method based on deep model is a technique which can convert image content into intuitive understandable semantic knowledge through deep model,attracting wide atten-tion at home and abroad.The target of image semantic analysis method can be divided into phrases,multiple tags,and statements.This paper introduced the research status of the above methods and their advantages,and analyzed the fea-tures of the image during the process of knowledge acquisition and the existing problems,including the structural fea-tures of convolutional neural network and the recurrent neural network.From the aspects such as model structure and connection,this paper analyzed the research hotspot and the cases,then analyzed the differences between academia and industry,and adopted image sentence conversion to excute a discriminant comparison.Finally,this paper drew a conclu-sion and gave its hope for the images semantic analysis method with deep model.
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