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Image annotation based on multi-view robust spectral clustering
Journal of Visual Communication and Image Representation ( IF 2.6 ) Pub Date : 2020-12-24 , DOI: 10.1016/j.jvcir.2020.103003
Mona Zamiri , Hadi Sadoghi Yazdi

Nowadays, image annotation has been a hot topic in the semantic retrieval field due to the abundant growth of digital images. The purpose of these methods is to realize the content of images and assign appropriate keywords to them. Extensive efforts have been conducted in this field, which effectiveness is limited between low-level image features and high-level semantic concepts. In this paper, we propose a Multi-View Robust Spectral Clustering (MVRSC) method, which tries to model the relationship between semantic and multi-features of training images based on the Maximum Correntropy Criterion. A Half-Quadratic optimization framework is used to solve the objective function. According to the constructed model, a few tags are suggested based on a novel decision-level fusion distance. The stability condition and bound calculation of MVRSC are analyzed, as well. Experimental results on real-world Flickr and 500PX datasets, and Corel5K confirm the superiority of the proposed method over other competing models.



中文翻译:

基于多视图鲁棒谱聚类的图像标注

如今,由于数字图像的大量增长,图像标注已成为语义检索领域的热门话题。这些方法的目的是实现图像的内容并为其分配适当的关键字。在该领域已经进行了广泛的努力,其有效性在低级图像特征和高级语义概念之间受到限制。在本文中,我们提出了一种多视图鲁棒谱聚类(MVRSC)方法,该方法试图基于最大熵准则对训练图像的语义和多特征之间的关系进行建模。半二次优化框架用于求解目标函数。根据构造的模型,基于新颖的决策级融合距离,提出了一些标签。并分析了MVRSC的稳定性条件和边界计算。在实际Flickr和500PX数据集以及Corel5K上的实验结果证实了该方法相对于其他竞争模型的优越性。

更新日期:2021-01-02
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