Beyond bag of latent topics: spatial pyramid matching for scene category recognition

Abstract

We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis (pLSA) based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ‘final’ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.

Keywords

Scene category recognition / Probabilistic latent semantic analysis / Bag-of-words / Adaptive boosting

References

[1] Freund, Y., Schapire, R.E., 1997. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55(1): 119−139. [

[2] Harris, C., Stephens, M., 1988. A combined corner and edge detector. Alvey Vision Conf., p.147−151. [

[3] Hofmann, T., 1999. Probabilistic latent semantic indexing. Proc. 22nd Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.50−57. [

[4] Hu, Z.H., Cai, Y.Z., Li, Y.G., , 2005. Data fusion for fault diagnosis using multi-class support vector machines. J. Zhejiang Univ.-Sci., 6A(10): 1030−1039. [

[5] Kadir, T., Brady, M., 2001. Saliency, scale and image description. Int. J. Comput. Vis., 45(2): 83−105. [

[6] Kwitt, R., Vasconcelos, N., Rasiwasia, N., 2012. Scene recognition on the semantic manifold. European Conf. on Computer Vision, p.359−372. [

[7] Lazebnik, S., Schmid, C., Ponce, J., 2006. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.2169−2178. [

[8] Li, F.F., Perona, P., 2005. A Bayesian hierarchical model for learning natural scene categories. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.524−531. [

[9] Liu, J.G., Shah, M., 2007. Scene modeling using coclustering. IEEE Int. Conf. on Computer Vision, p.1−7. [

[10] Lowe, D.G., 2004. Distinctive image features from scaleinvariant keypoints. Int. J. Comput. Vis., 60(2): 91−110. [

[11] Lu, F.X., Yang, X.K., Zhang, R., , 2009. Image classification based on pyramid histogram of topics. IEEE Int. Conf. on Multimedia and Expo, p.398−401. [

[12] Lu, F.X., Yang, X.K., Lin, W.Y., , 2011. Image classification with multiple feature channels. Opt. Eng., 50(5): 057210.1−057210.9. [

[13] Matas, J., Chum, O., Urban, M., , 2004. Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput., 22(10): 761−767. [

[14] Mikolajczyk, K., Schmid, C., 2004. Scale & affine invariant interest point detectors. Int. J. Comput. Vis., 60(1): 63−86. [

[15] Oliva, A., Torralba, A., 2001. Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis., 42(3): 145−175. [

[16] Qi, X.B., Xiao, R., Li, C.G., , 2014. Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans. Patt. Anal. Mach. Intell., 36(11): 2199−2213. [

[17] Quelhas, P., Monay, F., Odobez, J., , 2007. A thousand words in a scene. IEEE Trans. Patt. Anal. Mach. Intell., 29(9): 1575−1589. [

[18] Shechtman, E., Irani, M., 2007. Matching local selfsimilarities across images and videos. IEEE Conf. on Computer Vision and Pattern Recognition, p.1−8. [

[19] Wang, Z.L., Feng, J.S., Yan, S.C., , 2013. Linear distance coding for image classification. IEEE Trans. Image Process., 22(2): 537−548. [

[20] Wu, J.X., 2012. Efficient HIK SVM learning for image classification. IEEE Trans. Image Process., 21(10): 4442−4453. [

[21] Wu, J.X., Rehg, J.M., 2011. CENTRIST: a visual descriptor for scene categorization. IEEE Trans. Patt. Anal. Mach. Intell., 33(8): 1489−1501. [

[22] Zhang, J.G., Marszałek, M., Lazebnik, S., , 2006. Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis., 73(2): 213−238. [