CAJ | 학술논문

大部分传统的基于距离尺度学习的图像标注方法假设训练样本之间存在确定的约束关系。这样的假设在小规模、精准的数据集上可以取得一定的效果。但是当数据量较大，每幅图像有多个标注且部分标注含有噪声时，这种过于理想的假设则不成立。提出了一种基于概率主题模型的距离尺度学习方法，通过概率主题模型挖掘这种模糊的、潜在的不确定辅助信息，并应用于后续的距离尺度学习中得到语义上距离尺度，改进基于搜索的图像标注性能。在 Flickr 数据集上的实验证明所提出方法比当前方法具有更好的图像自动标注性能。
대부분전통적기우거리척도학습적도상표주방법가설훈련양본지간존재학정적약속관계。저양적가설재소규모、정준적수거집상가이취득일정적효과。단시당수거량교대，매폭도상유다개표주차부분표주함유조성시，저충과우이상적가설칙불성립。제출료일충기우개솔주제모형적거리척도학습방법，통과개솔주제모형알굴저충모호적、잠재적불학정보조신식，병응용우후속적거리척도학습중득도어의상거리척도，개진기우수색적도상표주성능。재 Flickr 수거집상적실험증명소제출방법비당전방법구유경호적도상자동표주성능。
Most of the traditional approaches for image annotation based on distance metric learning generally suppose that the constraints on training data is explicit,which only works on small datasets with exact labels.As the scale of the dataset becomes larger and most images are accompanied by noisy labels,this ideal assumption will be not efficient.In this paper,we propose a novel distance metric learning method based on probabilistic topic model.The uncertain and latent side information can be mined by a probabilistic topic model and afterwards used in distance metric learning.The learned semantic distance metric then can be used in the search-based image annotation.Experiments on Flickr dataset demonstrate that the proposed model outperforms the state-of-the-art annotation methods.