Abstract
With the increment of images in modern time, scene classification becomes more significant and harder to be settled. Many models have been proposed to classify scene images such as Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). In this paper, we propose an improved method, which combines spatial and color features and bases on PLSA model. When calculating and quantizing spatial features, chain code is used in the process of feature extraction. At the same time, color features are extracted in every block region. The PLSA model is applied in the scene classification. Finally, the experiment results between PLSA and other models are compared. The results show that our method is better than many other state-of-the-art scene classification methods.
Chapter PDF
Similar content being viewed by others
Keywords
References
Biederman, I.: Aspects and extension of a theory of human image understanding. In: Computational Processes in Human Vision: an Inter-Disciplinary Perspective, New (1988)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)
Bosch, A., Muñoz, X., Martí, R.: Which is the best way to organize/classify images by content? Image and Vision Computing 25(6), 778–791 (2007)
Bosch, A., Zisserman, A., Muoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE Trans. Pattern Analysis and Machine Intelligence 30(4), 712–727 (2008)
Chen, S., Tian, Y.: Evaluating effectiveness of latent dirichlet allocation model for scene classification. In. In: 2011 20th Annual Wireless and Optical Communications Conference (WOCC), pp. 1–6. IEEE (2011)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proc. 22nd Annual Int’l. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 50–57 (1999)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1-2), 177–196 (2001)
Iivarinen, J., Visa, A.: Shape recognition of irregular objects. . In: Intelligent Robots and Computer Vision XV: Algorithms, Techniques, Active Vision, and Materials Handling, pp. 25–32. SPIE (1996)
Shimazaki, K., Nagao, T.: Scene classification using color and structure-based features. In: Sixth International Workshop on Computational Intelligence and Applications (IWCIA), pp. 211–216. IEEE (2013)
Fei-Fei Li, P.: Perona. 2005. A bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 524–531 (2005)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)
Quelhas, P., Monay, F., Odobez, J.M., et al.: Modeling scenes with local descriptors and latent aspects. In: ICCV, vol. 1, pp. 883–890 (2005)
Rasiwasia, N., Vasconcelos, N.: Latent Dirichlet Allocation Models for Image Classification. IEEE Trans. PAMI 35(11), 2665–2679 (2013)
Sun, J., Xu, H.: Contour-Shape recognition and retrival based on chain code. In: Proc. Computational Intelligence and Security, CIS 2009, vol. 1, pp. 349–352 (2009)
Schölkopf, B., Smola, A.J.: Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press (2002)
Wang, X.L., Xie, K.L.: A novel direction chain code-based image retrieval. In: Proceedings of the Fourth International Conference on Computer and Information Technology, CIT 2004, pp. 190–193 (2004)
Zhang, H., Berg, A.C., Maire, M., Malik, J.: SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2126–2136 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Zeng, P., Li, Z., Zhang, C. (2014). Scene Classification Using Spatial and Color Features. In: Shi, Z., Wu, Z., Leake, D., Sattler, U. (eds) Intelligent Information Processing VII. IIP 2014. IFIP Advances in Information and Communication Technology, vol 432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44980-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-662-44980-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44979-0
Online ISBN: 978-3-662-44980-6
eBook Packages: Computer ScienceComputer Science (R0)