基于多视图非负矩阵分解和语义共现的图像标注

Fuping Zhong, Lihong Ma
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引用次数: 4

摘要

图像标注旨在自动预测一组描述图像语义的相关关键字。基于最近邻(NN)的方法已经成功地应用于解决图像标注问题。本文提出了一种提高图像标注性能的新方法。首先,提出了一种基于多视图非负矩阵分解(multiview non-negative matrix factorization, MultiNMF)的关联反馈算法,以提高最近邻居查询过程中的检索性能。其次,提出一种基于语义共现(SC)的标注策略,有效调整标注关键词的顺序;在Corel5K数据集上的实验结果表明,该方法优于以往的同类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image annotation using multi-view non-negative matrix factorization and semantic co-occurrence
Image annotation aims to automatically predict a set of relevant keywords for an image that describe its semantics. Nearest Neighbor (NN) based methods have been successfully applied to address image annotation problems. In this paper,a novel method is introduced to improve the performance of annotating images. Firstly, we present a relevance feedback algorithm based on Multi-view non-negative matrix factorization (MultiNMF) to improve the retrieval performance during the process of querying the nearest neighbors. Secondly, a semantic co-occurrence (SC) based strategy is derived to effectively adjust the order of the annotated keywords. Experiment results on Corel5K dataset demonstrate that the proposed method outperforms those previous similar methods.
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