一种融合语义距离的最近邻图像标注方法

摘要/Abstract

摘要： 传统的基于最近邻的图像标注方法效果不佳,主要原因在于提取图像视觉特征时,损失了很多有价值的信息。提出了一种改进的最近邻分类模型。首先利用距离测度学习方法,引入图像的语义类别信息进行训练,生成新的语义距离；然后利用该距离对每一类图像进行聚类,生成多个类内的聚类中心；最后通过计算图像到各个聚类中心的语义距离来构建最近邻分类模型。在构建最近邻分类模型的整个过程中,都使用训练得到的语义距离来计算,这可以有效减少相同图像类内的变动和不同图像类之间的相似所造成的语义鸿沟。在ImageCLEF2012图像标注数据库上进行了实验,将本方法与传统分类模型和最新的方法进行了比较,验证了本方法的有效性。

关键词: 图像标注,特征提取,最近邻,距离测度学习,语义距离

Abstract: Most of the nearest neighbor (NN) based image annotation or classification methods do not achieve desired performances.The main reason is that much valuable information is lost when extracting visual features from image.A novel nearest neighbor method was proposed.Firstly,we obtained a new image semantic distance learned by distance metric learning (DML) using image class information,and then multiple clustering centers were formed based on this learned semantic distance.Finally,we constructed our NN model by calculating the distances between the image and these clusters.Our model can minimize the semantic gap for intra-class variations and inter-class similarities.Experimental results on image annotation task of ImageCLEF2012 confirm that our method is efficient and competitive compared with the traditional and state of the art classifiers.

Key words: Image annotation,Feature extraction,Nearest neighbor,Distance metric learning,Semantic distance

吴伟,高光来,聂建云. 一种融合语义距离的最近邻图像标注方法[J]. 计算机科学, 2015, 42(1): 297-302. https://doi.org/10.11896/j.issn.1002-137X.2015.01.066

WU Wei, GAO Guang-lai and NIE Jian-yun. Combination of Nearest Neighbor with Semantic Distance for Image Annotation[J]. Computer Science, 2015, 42(1): 297-302. https://doi.org/10.11896/j.issn.1002-137X.2015.01.066

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