基于内容检索的图像自动标注方法研究

计算机科学 ›› 2014, Vol. 41 ›› Issue (Z11): 119-122.

基于内容检索的图像自动标注方法研究

邓莉琼,郝向宁,夏鸣,李中宁

空军大连通信士官学校大连116600;空军大连通信士官学校大连116600;空军大连通信士官学校大连116600;空军大连通信士官学校大连116600

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家自然科学基金(61201339)资助

Image Annotation by Similarity Content-based Image Retrieval

DENG Li-qiong,HAO Xiang-ning,XIA Ming and LI Zhong-ning

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 图像标注技术是近年来的研究热点。为了更好地解决图像自动标注问题,提出了一个基于检索和重排序的标注方法。在检索阶段,通过使用基于MSF的全局特征对待标注图像进行基于内容的检索,从而在图像数据库中得到一系列的相似图像数据集；在重排序阶段,利用随机漫步方法对相似图像数据集的标注信息进行重排序,最终排序后的关键词则为待标注图像的标注信息。该方法不仅跳过了漫长的训练阶段,而且充分利用了网络上那些已经具有标注信息的图像,具有较好的稳定性和可扩展性。实验结果显示了该方法的有效性。

关键词: 图像标注,相似度检索,重排序,马尔科夫,基于内容

Abstract: Image annotation is an active research topic in recent years.In this paper,we targeted at solving the automatic image annotation problem in a novel search and refinement framework.In the search stage,we performed content-based image retrieval(CBIR) based on the MSF global feature to find similar images from image database.Then in the refinement stage,an algorithm using random walk with restarts is used to re-rank the annotations.The refinement keywords are used to annotate the uncaptioned image.This framework does not impose an training stage,but efficiently utilizes well-annotated images,and is potentially capable of dealing with unlimited vocabulary.The experiment results show the effectiveness and efficiency of the proposed approach.

Key words: Image annotation,Similarity search,Refinement,Markov,Content-based

邓莉琼,郝向宁,夏鸣,李中宁. 基于内容检索的图像自动标注方法研究[J]. 计算机科学, 2014, 41(Z11): 119-122. https://doi.org/

DENG Li-qiong,HAO Xiang-ning,XIA Ming and LI Zhong-ning. Image Annotation by Similarity Content-based Image Retrieval[J]. Computer Science, 2014, 41(Z11): 119-122. https://doi.org/

参考文献

[1] Chang E,Goh K,Sychay G,et al.CBSA:content-base soft annotation for multimodal image retrieval using bayes point machines[C]∥Proc.CVPR.2004
[2] Cusano C,Ciocca G,Schettini R.Image annotation using SVM[C]∥Proc.Internet imaging.2004:330-338
[3] Carneiro G,Vasconcelos N.A database centric view of semantic image annotation and retrieval[C]∥Proc.ACM SIGIR.2005:559-566
[4] Mori Y,Takahashi H,Oka R.Image-to-word transformationbased on dividing and vector quantizing images with words[C]∥MISRM.1999
[5] Barnard P D,de Freitas J F G,Forsyth D A.Object recognition as machine translation:learning a lexicon for a fixed image vocabulary[C]∥Proc.ECCV.2002:97-112
[6] Blei D M,Jordan M I.Modeling annotated data[C]∥Proc.ACM SIGIR.2003:127-134
[7] Yossi R,CAarlo T,Leonidas J G.The Earth Mover’s Distance as a Metric for Image Retrieval[C]∥Proceedings of the 2000 IEEE International Conference on Computer Vision.Bombay,India,2000:59-66
[8] Tong H H,Faloutsos C,Pan J Y.Fast Random Walk with Restart and Its Applications[C]∥Proc.ACM SIGKDD.2006
[9] Barnard P D,de Freitas J F G,Forsyth D A.Object recognition as machine translation:learning a lexicon for a fixed image vocabulary[C]∥Proc.ECCV.2002:97-112

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