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Image region annotation based on segmentation and semantic correlation analysis

机译：基于分割和语义相关性分析的图像区域标注

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摘要

The authors propose an image region annotation framework by exploring syntactic and semantic correlations among segmented regions in an image. A texture-enhanced image segmentation JSEG algorithm is first used to improve the pixel consistency in a segmented image region. Next, each region is represented by a set of image codewords, also known as visual alphabets, with each of them used to characterise certain low-level image features. A visual lexicon, with its vocabulary items defined as either a codeword or a co-occurrence of multiple alphabets, is formed and used to model middle-level semantic concepts. The concept classification models are trained by a maximal figure-of-merit algorithm with a collection of training images with multiple correlations, including spatial, syntactic and semantic relationship, between regions and their corresponding concepts. In addition, a region-semantic correlation model constructed with latent semantic analysis is used to correct the potentially wrong annotations by analysing the relationship between image region positions and labels. When evaluated on the Corel 5K dataset, the proposed image region annotation framework achieves accurate results on image region concept tagging as well as whole image based annotations.
机译：通过探索图像中分割区域之间的句法和语义相关性，作者提出了一种图像区域注释框架。首先使用纹理增强的图像分割JSEG算法来提高分割图像区域中的像素一致性。接下来，每个区域由一组图像代码字（也称为可视字母）表示，它们中的每一个用于表征某些低级图像特征。形成视觉词汇，其词汇定义为代码字或多个字母的同时出现，并用于对中层语义概念进行建模。通过最大品质因数算法对概念分类模型进行训练，该算法具有一系列训练图像的集合，这些训练图像在区域及其对应概念之间具有多种相关性，包括空间，句法和语义关系。另外，通过分析图像区域位置和标签之间的关系，使用通过潜在语义分析构建的区域语义相关模型来纠正潜在的错误注释。当在Corel 5K数据集上进行评估时，提出的图像区域注释框架可在图像区域概念标记以及基于整个图像的注释上获得准确的结果。

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