Abstract
Classifying scenes (such as mountains, forests) is not an easy task owing to their variability, ambiguity, and the wide range of illumination and scale conditions that may apply. Bag of features (BoF) model have achieved impressive performances in many famous databases (such as the 15 scene dataset). A main drawback of the BoF model is it disregards all information about the spatial layout of the features, leads to a limited descriptive ability. In this paper, we use co-occurrence matrix to implant the spatial relations between local features, and demonstrate that feature co-occurrence matrix (FCM) is a potential discriminative character to scenes classification. We propose three FCM based image representations for scenes classification. The experimental results show that, under equal protocol, the proposed method outperforms BoF model and Spatial Pyramid (SP) model and achieves a comparable performance to the state-of-the-art.
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References
Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 524–531. IEEE (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178. IEEE (2006)
Vogel, J., Schiele, B.: Semantic modeling of natural scenes for content-based image retrieval. Int. J. Comput. Vis. 72, 133–157 (2007)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1615–1630 (2005)
Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, Manchester, UK, vol. 15, p. 50 (1988)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60, 63–86 (2004)
Hamerly, G., Elkan, C.: Learning the k in k-means. Adv. Neural Inf. Process. Syst. 16, 281 (2004)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, pp. 1–2 (2004)
van Gemert, J.C., Geusebroek, J.-M., Veenman, C.J., Smeulders, A.W.M.: Kernel codebooks for scene categorization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 696–709. Springer, Heidelberg (2008)
Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Advances in Neural Information Processing Systems, pp. 2223–2231 (2009)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1794–1801. IEEE (2009)
Sharma, G., Jurie, F., Schmid, C.: Discriminative spatial saliency for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3506–3513. IEEE (2012)
Chen, Q., Song, Z., Hua, Y., Huang, Z., Yan, S.: Hierarchical matching with side information for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3426–3433. IEEE (2012)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man and Cybern. 3, 610–621 (1973)
Chang, C., Lin, C.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2011)
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large scale scene recognition from abbey to zoo. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp. 1–8. IEEE (2010)
Acknowledgement
This work was supported in part by Beijing Higher Education Young Elite Teacher Project under Grants YETP0514, and NSFC under Grants 61471024. Ling was supported in part by the US NSF Grants IIS-1218156 and IIS-1350521.
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Lang, H., Xi, Y., Hu, J., Du, L., Ling, H. (2015). Scene Classification by Feature Co-occurrence Matrix. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_36
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DOI: https://doi.org/10.1007/978-3-319-16628-5_36
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