Image Annotation with Nearest Neighbor Based on Semantic Information
作者单位：Department of Computer Science Inner Mongolia University
会议名称：《2015年中国智能自动化学术会议》
会议日期：2015年
学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程]
基 金：supported by Inner Mongolia NSF 2014MS0606
关 键 词：Image annotation Nearest neighbor Distance metric learning Entropy weight
摘 要：Most of the Nearest Neighbor(NN)-based image annotation methods do not achieve desired performances. The main reason is that much valuable information is lost when extracting visual features from image. In this paper, we propose a novel weighted NN-based method. Instead of using Euclidean distance, we learn a new distance metric with image semantic information to calculate the distance between the two images. Meanwhile, we utilize textual information of each image tagged by users to form weights of NN-based model. When introducing the semantic information, our method can minimize the semantic gap for intraclass variations and interclass similarities, and improve the annotation *** on image annotation dataset of Image CLEF2012 show that our method outperforms the traditional classifiers. Moreover, our method is simple,efficient, and competitive compared with state-of-the-art learning-based models.