Abstract
pLSA has been successfully used in scene classification as an intermediate representation of images, but it didn’t utilize the spatial information of an image which is important for scene classification tasks. To improve the accuracy of classification, we proposed a new method which incorporates spatial information coming from neighbor words and topics’ position into pLSA. Finally, an image can be represented by the position distribution of each latent topic, and subsequently, we train a classifier on the topics’ position distribution vector for each image. Besides, the traditional fold-in heuristic way of pLSA is not necessary and more sophisticated supervised pLSA can be adopted when our no-fold-in way is used, whichalso givesan accuracy improvement.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: CAIVD Workshop, ICCV (January 1998)
Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.J.: Image classification for content-based indexing. IEEE Trans. on Image Processing 10(1), 117–130 (2001)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV, pp. 1–22 (2004)
Blei, D., Andrew, Y., Jordan, M.: Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1020 (2003)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learning 42, 177–196 (2001)
Zhai, C.X., Velivelli, A., Yu, B.: A cross-collection mixture model for comparative text mining. In: Proceedings of the Tenth ACM SIGKDD International Conference on KDD, Seattle, WA, USA, August 22-25 (2004)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering Objects and Their Locations in Images. In: Proc. ICCV, pp. 370–377 (October 2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (June 2006)
Fei-Fei, L., Perona, P.: A Bayesian Hierarchical Model for Learning Natural Scene Categories. In: Proc. IEEE CS Conf. CVPR, pp. 524–531 (2005)
Quelhas, P., Monay, F., Odobez, J.M., Gatica-Perez, D., Tuytelaars, T., Van Gool, L.: Modeling Scenes with Local Descriptors and Latent Aspects. In: Proc. Int’l Conf. Computer Vision, pp. 883–890 (October 2005)
Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning Object Categories from Google’s Image Search. In: Proc. Int’l Conf. Computer Vision, pp. 1816–1823 (October 2005)
Bosch, A., Zisserman, A., Munoz, X.: Scene classification using a hybrid generative/dicriminative approach. IEEE Trans. on PAMI (2008)
Ergul, E., Arica, N.: Scene Classification Using Spatial Pyramid of Latent Topics. In: ICPR (2010) 1991, 1992
Lowe, D.: Distinctive Image Features from Scale Invariant Keypoints. Int’l J. Computer Vision 60(2), 91–110 (2004)
Zhang, E., Mayo, M.: Improving Bag-of-Words Model with Spatial Information. In: Proc. IEEE IVCNZ, pp. 1–6 (2010)
Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local Binary Patterns and Its Application to Facial Image Analysis: A Survey. IEEE Transactions on Systems, Man, and Cybernetics 41(6), 765–781 (2011)
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
Huang, F., Jing, X., Sun, S., Lu, Y. (2013). Incorporate Spatial Information into pLSA for Scene Classification. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2012. Communications in Computer and Information Science, vol 320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35795-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-35795-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35794-7
Online ISBN: 978-3-642-35795-4
eBook Packages: Computer ScienceComputer Science (R0)