南京大学学报(自然科学版) ›› 2018, Vol. 54 ›› Issue (6): 11931205.doi: 10.13232/j.cnki.jnju.2018.06.015
赵天龙1,2,刘 峥1,2*,韩慧健1,2,张彩明3,4
Zhao Tianlong1,2,Liu Zheng1,2*,Han Huijian1,2,Zhang Caiming3,4
摘要: 传统的图像标签推荐方法通过对图像视觉内容的分析计算标签与图像的相关度,完成标签推荐任务. 而社会网络图像具有丰富的元数据,例如图像所属群组、地理位置等,充分利用这些元数据对于提高标签推荐的准确性具有积极意义. 提出一种基于二分图的个性化图像标签推荐算法,通过充分挖掘图像、群组、地理位置与标签的关系,针对用户提供的少量标签进行个性化图像标签推荐. 该算法建立了图像-标签、群组-标签、地理位置-标签等三个二分图模型,考虑到每个标签的重要性不同,引入TF-IDF(Term Frequency-Inverse Document Frenquency)技术对标签进行加权处理. 利用二分图将初始标签分值进行信息扩散,计算出最终标签分值向量,并将该向量中分值较高的标签作为推荐结果. 实验结果表明,融合了图像与群组、地理位置等元数据的个性化图像标签推荐结果的NDCG(Normalized Discounted Cumulative Gain)值优于仅单方面考虑图像、群组以及地理位置的标签推荐结果.
中图分类号:
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