Natural scene recognition using weighted histograms of gradient orientation descriptor

Abstract

The automatic recognition of the contents of a scene is an important issue in the computer vision field. Though considerable progress has been made, the complexity of scenes remains an important challenge to computer vision research. Most of the previous scene recognition models are based on the so-called “bag of visual words” method, which uses some clustering method to quantize the numerous local region descriptors into a codebook. The size of the codebook and the selection of initial clustering center have great influence on the performance. Furthermore, the big size of the codebook has high computational cost and memory consumption. To overcome these drawbacks, we present an unsupervised natural scene recognition approach that is not based on the “bag of visual words” method. This approach works by creating multiple resolution images and partitioning them into sub-regions at different scales. The descriptors of all sub-regions in the same resolution image are directly concatenated for support vector machine (SVM) classifiers. To represent images more effectively, we present a new visual descriptor: weighted histograms of gradient orientation (WHGO). We evaluate our approach on three data sets: the 8 scene categories of Oliva et al., the 13 scene categories of Fei-Fei et al. and the 15 scene categories of Lazebnik et al. Experiments show that the WHGO descriptor outperforms the classical scale invariant feature transform (SIFT) descriptor in natural scene recognition, and our approach achieves good performances with respect to the state of the art methods.

Keywords

natural scene recognition / weighted histograms of gradient orientation (WHGO) descriptor / multi-resolution / multi-scale partition / feature combination

References

[1] Torralba A. Contextual priming for object detection. International Journal of Computer Vision, 2003, 53(2): 169-191

[2] Vogel J, Schiele B. Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 2007, 72(2): 133-157

[3] Kivinen J J, Sudderth E B, Jordan M I. Learning multiscale representations of natural scenes using Dirichlet processes. In: Proceedings of the 11th International Conference on Computer Vision. 2007, 1-8

[4] Liu J, Shah M. Scene modeling using co-clustering. In: Proceedings of the 11th International Conference on Computer Vision. 2007, 1-7

[5] Rasiwasia N, Vasconcelos N. Scene classification with lowdimensional semantic spaces and weak supervision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2008, 1-6

[6] Smeulders A W, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1349-1380

[7] Szummer M, Picard R. Indoor-outdoor image classification. In: Proceedings of IEEE International Workshop on Content-based Access of Image and Video Database. 1998, 42-51

[8] Oliva A, Torralba A. Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision, 2001, 42(3): 145-175

[9] Mikolajczyk K, Schmid C. Scale and affine invariant interest point detectors. International Journal of Computer Vision, 2004, 60(1): 63-86

[10] Lowe D. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110

[11] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 2(4): 509-522

[12] Lazebnik S, Schmid C, Ponce J. A Sparse texture representation using affine-invariant regions. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2003, 2: 319-324

[13] Bosch A, Zisserman A, Munoz X. Scene classification via pLSA. In: Proceedings of the 9th European Conference on Computer Vision. 2006, 517-530

[14] Bosch A, Zisserman A, Muñoz X. Scene classification using a hybrid generative/discriminative approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(4): 712-727

[15] Fei-Fei L, Perona P. A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of IEEE Computer Society International Conference on Computer Vision and Pattern Recognition. 2005, 2: 524-531

[16] Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Computer Society International Conference on Computer Vision and Pattern Recognition. 2006, 2169-2178

[17] Ulrich I, Nourbakhsh I R. Appearance-based place recognition for topological localization. In: Proceedings of IEEE International Conference on Robotics and Automation. 2006, 2: 1023-1029

[18] Pronobis A, Caputo B, Jensfelt P. A discriminative approach to robust visual place recognition. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006, 7

[19] Mikolajczyk K, Schmid C. Performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630

[20] Chang C C, Lin C J. LIBSVM: a library for support vector machines, 2001. Software available at: http://www.csie.ntu.edu.tw/∼cjlin/libsvm

[21] Zhang J, Marszalek 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, 73(2): 213-238

[22] Gehler P, Nowozin S. On feature combination for multiclass object classification. In: Proceedings of IEEE 12th International Conference on Computer Vision. 2009, 221-228

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg