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首页> 外文期刊>Journal of visual communication & image representation >Automatic image annotation based on Gaussian mixture model considering cross-modal correlations
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Automatic image annotation based on Gaussian mixture model considering cross-modal correlations

机译：考虑跨模态相关的基于高斯混合模型的自动图像标注

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Automatic image annotation has been an active topic of research in the field of computer vision and pattern recognition for decades. In this paper, we present a new method for automatic image annotation based on Gaussian mixture model (GMM) considering cross-modal correlations. To be specific, we first employ GMM fitted by the rival penalized expectation-maximization (RPEM) algorithm to estimate the posterior probabilities of each annotation keyword. Next, a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity by seamlessly integrating the information from both image low level visual features and high level semantic concepts together, which can effectively avoid the phenomenon that different images with the same candidate annotations would obtain the same refinement results. Followed by the rank-two relaxation heuristics over the built label similarity graph is applied to further mine the correlation of the candidate annotations so as to capture the refining annotation results, which plays a crucial role in the semantic based image retrieval. The main contributions of this work can be summarized as follows: (1) Exploiting GMM that is trained by the RPEM algorithm to capture the initial semantic annotations of images. (2) The label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels. (3) Refining the candidate set of annotations generated by the GMM through solving the max-bisection based on the rank-two relaxation algorithm over the weighted label graph. Compared to the current competitive model SGMM-RW, we can achieve significant improvements of 4% and 5% in precision, 6% and 9% in recall on the Corel5k and Mirflickr25k, respectively. (C) 2017 Elsevier Inc. All rights reserved.
机译：几十年来，自动图像标注一直是计算机视觉和模式识别领域研究的一个活跃主题。在本文中，我们提出了一种基于高斯混合模型（GMM）并考虑跨模态相关性的自动图像标注新方法。具体来说，我们首先采用竞争对手的惩罚性期望最大化（RPEM）算法拟合的GMM来估算每个注释关键字的后验概率。接下来，通过标签相似度和视觉相似度的加权线性组合，将来自图像低层视觉特征和高级语义概念的信息无缝地整合在一起，构建标签相似度图，可以有效地避免相同图像不同的现象。候选注释将获得相同的优化结果。其次，通过对构建的标签相似度图进行二级松弛启发式，进一步挖掘候选注释的相关性，以捕获精炼注释结果，这在基于语义的图像检索中起着至关重要的作用。这项工作的主要贡献可以归纳如下：（1）利用由RPEM算法训练的GMM来捕获图像的初始语义注释。 （2）标签相似度图由标签相似度和与相应标签相关联的图像的视觉相似度的加权线性组合构成。 （3）通过基于加权标签图上的秩2松弛算法求解最大二等分，优化GMM生成的注释的候选集。与目前的竞争机型SGMM-RW相比，我们在Corel5k和Mirflickr25k上的精度分别达到4％和5％的显着提高，召回率分别为6％和9％的显着提高。 （C）2017 Elsevier Inc.保留所有权利。

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