Abstract
In this paper, we propose a method for image retrieval on the web. In this task, we focus on abstract words that do not directly link to images that we want. For example, a user might use a query “summer” to retrieve images of “fireworks” or “a white sand beach with the sea”. In this case retrieval systems need to infer direct words for the images from the abstract query of the user. In our method, we extract related words about a query from the web first. Second, we retrieve images from the web by using the extracted words. Then, a user selects relevant images from the retrieved images. Next, the system computes a similarity between selected images and other images and ranks the images on the basis of the similarity. We use the Earth Mover’s Distance as the similarity. The experimental result shows the effectiveness of our method that uses text and image information for the image retrieval process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kato, M., Ohshima, H., Oyama, S., Tanaka, K.: “likely” image search: Web image search using term sets representing typical features extracted from social tagging information. In: Proceedings of Data Engineering Workshop (DEWS 2008), IEICE (2008)
Freng, Y., Lapata, M.: Automatic image annotation using auxiliary text information. In: Proceedings of ACL 2008: HLT, Columbus, Ohio, June 2008, pp. 272–280. Association for Computational Linguistics (2008)
Gudivada, V., Raghavan, V.: Content-based image retrieval-systems. IEEE Comput. 28(9), 18–22 (1995)
Kushima, K., Akama, H., Konya, S., Yamamuro, M.: Content based image retrieval techniques based on image features. Transactions of Information Processing Society of Japan 40(SIG3(TOD1), 171–184 (1999)
Sezaki, N., Kise, K.: Tagging system using co-occurrence of tags and similar images. In: Proceedings of Data Engineering Workshop (DEWS 2008). IEICE (2008)
Barthel, K.U., Richter, S., Goyal, A., Fllmann, A.: Improved image retrieval using visual sorting and semi-automatic semantic categorization of images. In: MMIU 2008, VISIGRAPP 2008 (2008)
Rui, Y., Huang, T.S., Mehrotra, S.: Relevance feedback techniques in interactive content-based image retrieval. In: Proceedings of Storage and Retrieval of Image and Video Databases VI (SPIE), pp. 25–36 (1998)
Rubner, Y., Tomasi, C., Guibas, L.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
Ikehara, S., Miyazaki, M., Shirai, S., Yokoo, A., Nakaiwa, H., Ogura, K., Ooyama, Y., Hayashi, Y. (eds.): Goi-Taikei. A Japanese Lexicon. Iwanami Shoten (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shimada, K., Ishikawa, S., Endo, T. (2009). Web Image Retrieval for Abstract Queries Using Text and Image Information. In: Lee, G.G., et al. Information Retrieval Technology. AIRS 2009. Lecture Notes in Computer Science, vol 5839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04769-5_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-04769-5_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04768-8
Online ISBN: 978-3-642-04769-5
eBook Packages: Computer ScienceComputer Science (R0)