Abstract
Scene categorization plays an important role in computer vision, image content understanding, and image retrieval. In this paper, back-propagation neural network (BPN) is served as the basic classifier for multi-class scene/image categorization. Four features, namely, SPM (spatial pyramid appearance descriptor represented by scale invariant feature transform), PHOG (pyramid histogram of oriented gradient), GIST, and HWVP (hierarchical wavelet packet transform) are selected as the basic inputs of BPNs. They are the appearance, shape and texture descriptors respectively. For an M (M>2) classes scene categorization problem, we cascade M one-versus-all BPNs to determine the accurate label of an image. An offline multi-class Adaboost algorithm is proposed to fuse multiple BPN classifiers trained with complementary features to improve scene categorization performance. Experimental results on the widely used Scene-13 and Sport Event datasets show the effectiveness of the proposed boosted BPN based scene categorization approach. Scene categorization performances of BPN classifiers with input features: SPM, PHOG, GIST and HWVP, boosted BPN classifiers of each of the four features, and the boosted classifiers of all the four features are given. Relationships of boosted classifiers number and the scene categorization performance are also discussed. Comparisons with some existing scene categorization methods using the authors’ datasets further show effectiveness of the proposed boosted BPN based approach.
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
Monay, F., Gatica-Perez, D.: PLSA-based image auto-annotation:constraining the latent space. In: Proc. ACM Multimedia (2004)
Sudderth, E., Torralba, A., Freeman, W., Willsky, A.: Describing visual scenes using transformed dirichlet processes. In: NIPS (2005)
Li, F., Perona, P.: A Bayesian hierarchy model for learning natural scene categories. In: Proc. CVPR (2005)
Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering object categories in image collections. In: Proc. ICCV (2005)
Zheng, Y., Zhao, M., Neo, S., Chua, T., Tian, Q.: Visual synset: towards a higher-level visual representation. In: Proc. CVPR (2008)
Zhang, J., MarszaÃlek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. International Journal of Computer Vision (2007)
Bosch, A., Zisserman, A., Munoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE TPAMI 30(4), 712–727 (2008)
Li, J., Wang, J.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1075–1088 (2003)
Bi, J., Chen, Y., Wang, J.: A Sparse Support Vector Machine Approach to Region-Based Image Categorization. In: Proc. CVPR (2005)
Larlus, D., Jurie, F.: Combining appearance models and markov random fields for category level object segmentation. In: Proc. CVPR (2008)
Quattoni, A., Collins, M., Darrell, T.: Conditional random fields for object recognition. In: NIPS (2004)
Galleguillos, C., Rabinovich, A., Belongie, S.: Object categorization using co-occurrence, location and appearance. In: Proc. CVPR (2008)
Cao, L., Li, F.: Spatially coherent latent topic model for concurrent object segmentation and classification. In: Proc. ICCV (2007)
Holub, A., Perona, P.: A discriminative framework for modeling object classes. In: Proc. ICCV (2005)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV (2) (2004)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proc. ECCV (2004)
Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: Proc. CVPR (2005)
Wang, G., Zhang, Y., Li, F.: Using dependent regions for object categorization in a generative framework. In: Proc. CVPR 2006 (2006)
Savarese, S., Winn, J., Criminisi, A.: Discriminative object class models of appearance and shape by correlations. In: Proc. CVPR 2006, pp. 2033–2040 (2006)
Teh, Y., Jordan, M., Beal, M., Blei, D.: Hierarchical Dirichlet processes. Journal of the American Statistical Association (2006)
Gosselin, P., Cord, M., Philipp-Foliguet, S.: Combining visual dictionary, kernel-based similarity and learning strategy for image category retrieval. Computer Vision and Image Understanding 110, 403–417 (2008)
Wu, L., Hu, Y., Li, M., Yu, N., Hua, X.: Scale-invariant visual language modeling for object categorization. IEEE Trans. Multimedia 11(2), 286–294 (2009)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proc. CIVR (2007)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. CVPR (2006)
Torralba, A., William, K., Freeman, T., Rubin, M.: Context-based vision system for place and object recognition. In: Proc. ICCV 2003 (2003)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42(3), 145–175 (2001)
Qian, X., Liu, G., Guo, D., Li, Z., Wang, Z., Wang, H.: Object categorization using hierarchical wavelet packet texture descriptors. In: Proc. ISM 2009, pp. 44–51 (2009)
Zhang, H., Berg, A., Maire, M., Malik, J.: Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In: Proc. CVPR (2006)
Ou, G., Murphey, Y.: Multi-class pattern classification using neural networks. Pattern Recognition 40, 4–18 (2007)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Freud, Y., Schapire, R.: Experiments with a new boosting algorithms. In: Machine Learning: Proceedings of the 13th International Conference (1996)
Li, L., Li, F.: What, where and who? Classifying events by scene and object recognition. In: Proc. ICCV (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qian, X. et al. (2010). Scene Categorization Using Boosted Back-Propagation Neural Networks. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-15702-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15701-1
Online ISBN: 978-3-642-15702-8
eBook Packages: Computer ScienceComputer Science (R0)