Image recommendation based on keyword relevance using absorbing Markov chain and image features

被引：11
作者：
Sejal D. [1 ]
Rashmi V. [1 ]
Venugopal K.R. [1 ]
Iyengar S.S. [2 ]
Patnaik L.M. [3 ]
机构：
[1] Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru
[2] Florida International University, Miami
[3] National Institute of Advanced Studies, Bengaluru
来源：
International Journal of Multimedia Information Retrieval | 2016年 / 5卷 / 3期
关键词：
Annotation-based image retrieval; Content-based image retrieval; Image annotation; Image recommendation;
D O I：
10.1007/s13735-016-0104-9
中图分类号：
学科分类号：
摘要：
Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user’s requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user’s input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images. © 2016, Springer-Verlag London.
引用
收藏
页码：185 / 199
页数：14
相关论文
共 39 条
[1]  
Akbas E., Vural F.T.Y., Automatic image annotation by ensemble of visual descriptors. In: CVPR’07: the proceedings of IEEE conference on computer vision and pattern recognition, pp 1–8, (2007)
[2]  
Bartolini I., Ciaccia P., Multi-dimensional keyword-based image annotation and search. In: The Proceedings of the 2nd international workshop on keyword search on structured data, pp 5–10, (2010)
[3]  
Wang C., Jing F., Zhang L., Zhang H.-J., Content-based image annotation refinement. In: CVPR’07: the proceedings of IEEE conference on computer vision and pattern recognition, pp 1–8, (2007)
[4]  
Li J., Wang J.Z., Real-time computerized annotation of pictures, IEEE Trans Pattern Anal Mach Intell, 30, 6, pp. 985-1002, (2008)
[5]  
Makadia A., Pavlovic V., Kumar S., A new baseline for image annotation, Comput Vis ECCV, 2008, pp. 316-329, (2008)
[6]  
Verma Y., Jawahar C.V., Image annotation using metric learning in semantic neighbourhoods, Comput Vis ECCV, 2012, pp. 836-849, (2012)
[7]  
Wang C., Blei D., Li F.-F., Simultaneous image classification and annotation. In: CVPR 2009: the proceedings of IEEE conference on computer vision and pattern recognition, pp 1903–1910, (2009)
[8]  
international conference on computer vision, (2009)
[9]  
Stevenson K., Leung C., Comparative evaluation of web image search engines for multimedia applications. In: ICME 2005: the proceedings of IEEE international conference on multimedia and expo, pp 4–14, (2005)
[10]  
Smyth B., A community-based approach to personalizing web search, IEEE J Comput, 40, 8, pp. 42-50, (2007)
1234