Abstract
In recent years, due to the wide spread of photo sharing Web sites such as Flickr and Picasa, we can put our own photos on the Web and show them to the public easily. To make the photos searched for easily, it is common to add several keywords which are called as “tags” when we upload photos. However, most of the tags are added one by one independently without much consideration of association between the tags. Then, in this paper, as a preparation for realizing simultaneous recognition of nouns and adjectives, we examine visual relationship between tags, particularly noun tags and adjective tags, by analyzing image features of a large number of tagged photos in social media sites on the Web with mutual information. As a result, it was turned out that mutual information between some nouns such as “car” and “sea” and adjectives related to color such as “red” and “blue” was relatively high, which showed that their relations were stronger.
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
Berg, T.L., Berg, A.C., Shih, J.: Automatic Attribute Discovery and Characterization from Noisy Web Data. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 663–676. Springer, Heidelberg (2010)
Parikh, D., Grauman, K.: Interactively building a discriminative vocabulary of nameable attributes. In: Proc. of IEEE Computer Vision and Pattern Recognition (2011)
Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: Proc. of IEEE Computer Vision and Pattern Recognition, pp. 1778–1785 (2009)
Dhar, S., Ordonez, V., Berg, T.L.: High level describable attributes for predicting aesthetics and interestingness. In: Proc. of IEEE Computer Vision and Pattern Recognition, pp. 1657–1664 (2011)
Yanai, K., Barnard, K.: Image region entropy: A measure of “visualness” of web images associated with one concept. In: Proc. of ACM International Conference Multimedia (2005)
Kawakubo, H., Akima, Y., Yanai, K.: Automatic construction of a folksonomy-based visual ontology. In: Proc. of International Symposium on Multimedia, pp. 330–335 (2010)
Yanai, K., Kawakubo, H., Qiu, B.: A visual analysis of the relationship between word concepts and geographical locations. In: Proceedings of the ACM International Conference on Image and Video Retrieval (2009)
Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(8), 800–810 (2001)
Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Proc. of ECCV Workshop on Statistical Learning in Computer Vision, pp. 59–74 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems, pp. 577–584 (2003)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 43, 177–196 (2001)
Cilibrasi, R.L., Vitanyi, P.M.B.: The google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kohara, Y., Yanai, K. (2013). Visual Analysis of Tag Co-occurrence on Nouns and Adjectives. In: Li, S., et al. Advances in Multimedia Modeling. MMM 2013. Lecture Notes in Computer Science, vol 7732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35725-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-35725-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35724-4
Online ISBN: 978-3-642-35725-1
eBook Packages: Computer ScienceComputer Science (R0)