Abstract
Scene classification is an important issue in computer vision area. However, it is still a challenging problem due to the variability, ambiguity, and scale change that exist commonly in images. In this paper, we propose a novel hypergraph-based modeling that considers the higher-order relationship of semantic attributes in a scene and apply it to scene classification. By searching subnetworks on a hypergraph, we extract the interaction subnetworks of the semantic attributes that are optimized for classifying individual scene categories. In addition, we propose a method to aggregate the expression values of the member semantic attributes which belongs to the explored subnetworks using the transformation method via likelihood ratio based estimation. Intensive experiment shows that the discrimination power of the feature vector generated by the proposed method is better than the existing methods. Consequently, it is shown that the proposed method outperforms the conventional methods in the scene classification task.
Chapter PDF
References
von Ahn, L.: Games with a purpose. Computer 39(6), 92–94 (2006)
Bo, L., Ren, X., Fox, D.: Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms. MIT Press (2011)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification using a hybrid Generative/Discriminative approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(4), 712–727 (2008)
Canny, J.: A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proc. of ECCV Workshop on Statistical Learning in Computer Vision, pp. 1–22 (May 2004)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (June 2009)
Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3(2), 185–205 (2005)
Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge 2007 (VOC 2007) results (2007), http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/
Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: Proc. of IEEE International Conference on Computer Vision, vol. 2, pp. 524–531 (October 2005)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (June 2008)
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Tech. Rep. 7694 (March 2007)
Kleinberg, E.M.: Stochastic discrimination. Annals of Mathematics and Artificial Intelligence 1, 207–239 (1990)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (June 2006)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)
Lew, M.S.: Principles of visual information retrieval. Springer, London (2001)
Li, L.J., Fei-Fei, L.: What, where and who? classifying event by scene and object recognition. In: Proc. of IEEE International Conference on Computer Vision, pp. 1–8 (October 2007)
Li, L.J., Su, H., Xing, E., Fei-Fei, L.: Object bank: A high-level image representation for scene classification and semantic feature sparsification. In: Advances in Neural Information Processing Systems, pp. 1378–1386. MIT Press (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Lu, Y., Liu, P.Y., Xiao, P., Deng, H.W.: Hotelling’s t2 multivariate profiling for detecting differential expression in microarrays. Bioinformatics 21(14), 3105–3113 (2005)
Rasiwasia, N., Vasconcelos, N.: Holistic context models for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(5), 902–917 (2012)
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. International Journal of Computer Vision 77(1-3), 157–173 (2008)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proc. of IEEE International Conference on Computer Vision, vol. 2, pp. 1470–1477 (October 2003)
Su, Y., Jurie, F.: Improving image classification using semantic attributes. International Journal of Computer Vision 100(1), 59–77 (2012)
Voloshin, V.I.: Introduction to graph and hypergraph theory. Nova Science Publishers, Hauppauge (2009)
Wu, J., Rehg, J.: Beyond the euclidean distance: Creating effective visual codebooks using the histogram intersection kernel. In: Proc. of IEEE International Conference on Computer Vision, pp. 630–637 (September 2009)
Xiao, J., Hays, J., Ehinger, K., Oliva, A., Torralba, A.: SUN database: Large-scale scene recognition from abbey to zoo. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3485–3492 (2010)
Zhang, B.T.: Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory. IEEE Computational Intelligence Magazine 3(3), 49–63 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Choi, SW., Lee, C.H., Park, I.K. (2014). Scene Classification via Hypergraph-Based Semantic Attributes Subnetworks Identification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8695. Springer, Cham. https://doi.org/10.1007/978-3-319-10584-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-10584-0_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10583-3
Online ISBN: 978-3-319-10584-0
eBook Packages: Computer ScienceComputer Science (R0)