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Adaptive all-season image tag ranking by saliency-driven image pre-classification

机译：通过显着性驱动的图像预分类进行自适应的全季节图像标签排名

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摘要

Social image tag ranking has emerged as an important research topic due to its application on web image search. This paper presents an adaptive all-season tag ranking algorithm which can handle the images with and without distinct object(s) using different tag ranking strategies. Firstly, based on saliency map derived from the visual attention model, a linear SVM is trained to pre-classify an image as attentive or non-attentive category by using the gray histogram descriptor on the corresponding saliency map. Then, an image with distinct object is processed by the tag saliency ranking algorithm emphasizing distinct object, which combines image saliency map with sparse representation based multi-instance learning algorithm. On the other hand, an image without distinct object can be processed by the tag relevance ranking algorithm via the sparse representation based neighbor-voting strategy. Such adaptive all-season tag ranking strategy can be regarded as taking full advantage of existing tag ranking paradigms. Experiments conducted on well-known image data sets demonstrate the effectiveness of the proposed framework.
机译：社交图像标签排名由于其在Web图像搜索中的应用而成为一个重要的研究主题。本文提出了一种自适应的全季节标签排名算法，该算法可以使用不同的标签排名策略来处理带有或不带有不同对象的图像。首先，基于从视觉注意力模型导出的显着图，通过使用相应显着图上的灰色直方图描述符，训练线性SVM将图像预分类为注意力或非注意力类别。然后，通过强调显着对象的标签显着性排序算法对具有显着对象的图像进行处理，该算法将图像显着性图与基于稀疏表示的多实例学习算法相结合。另一方面，标签相关性排序算法可以通过基于稀疏表示的邻居投票策略来处理没有明显对象的图像。这种自适应的全季节标签排名策略可以被视为充分利用了现有标签排名范例。对知名图像数据集进行的实验证明了所提出框架的有效性。

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