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Non-parametric e-mixture of Density Functions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Position Gradient and Plane Consistency Based Feature Extraction

Authors : Sujan Chowdhury, Brijesh Verma, Ligang Zhang

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Labeling scene objects is an essential task for many computer vision applications. However, differentiating scene objects with visual similarity is a very challenging task. To overcome this challenge, this paper proposes a position gradient and plane consistency based feature which is designed to distinguish visually similar objects and improve the overall labeling accuracy. Using the proposed feature we can differentiate objects with the same histogram of the gradient as well as we can differentiate horizontal and vertical objects. Integrating the proposed feature with low-level texture features and a neural network classifier, we achieve a superior performance (82 %) compared to state-of-the-art scene labeling methods on the Stanford background dataset.

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previous chapter An Effective Approach for Automatic LV Segmentation Based on GMM and ASM
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Literature
1.
Shotton, J., Winn, J., Rother, C., Criminisi, A.: Textonboost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. J. Comput. Vis. 81, 2–23 (2009)CrossRef
2.
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915–1929 (2013)CrossRef
3.
Bu, S., Han, P., Liu, Z., Han, J.: Scene parsing using inference Embedded Deep Networks. Pattern Recogn. 59, 188–496 (2016)CrossRef
4.
Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: 12th International Conference on Computer Vision, pp. 1–8 (2009)
5.
Grangier, D., Bottou, L., Collobert, R.: Deep convolutional networks for scene parsing. In: ICML 2009 Deep Learning Workshop (2009)
6.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRef
7.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
8.
Munoz, D., Bagnell, J., Hebert, M.: Stacked hierarchical labeling. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 57–70. Springer, Heidelberg (2010)CrossRef
9.
He, X., Zemel, R.S., Carreira-Perpiñán, M.Á.: Multiscale conditional random fields for image labeling. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-695–II-702 (2004)
10.
Socher, R., Huval, B., Bath, B., Manning, C.D., Ng, A.Y.: Convolutional-recursive deep learning for 3D object classification. In: Advances in Neural Information Processing Systems, pp. 665–673 (2012)
11.
Kumar, M.P., Koller, D.: Efficiently selecting regions for scene understanding. In: Computer Vision and Pattern Recognition (CVPR), pp. 3217–3224 (2010)
12.
Tighe, J., Lazebnik, S.: SuperParsing: scalable nonparametric image parsing with superpixels. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 352–365. Springer, Heidelberg (2010)CrossRef
13.
Yan, J., Yu, Y., Zhu, X., Lei, Z., Li, S.Z.: Object detection by labeling superpixels. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5107–5116 (2015)
14.
Jun, W., Chaolliang, Z., Shirong, L., Jian, W.: Outdoor scene labeling using deep convolutional neural networks. In: Control Conference (CCC), pp. 3953–3958 (2015)
15.
Liang, M., Hu, X., Zhang, B.: Convolutional neural networks with intra-layer recurrent connections for scene labeling. In: Advances in Neural Information Processing Systems, pp. 937–945 (2015)
16.
Byeon, W., Breuel, T.M., Raue, F., Liwicki, M.: Scene labeling with LSTM recurrent neural networks. In: Computer Vision and Pattern Recognition, pp. 3547–3555 (2015)
17.
Gould, S.: Multiclass pixel labeling with non-local matching constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2783–2790 (2012)
18.
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012)CrossRef
19.
Lempitsky, V., Vedaldi, A., Zisserman, A.: Pylon model for semantic segmentation. In: Advances in Neural Information Processing Systems, pp. 1485–1493 (2011)
20.
Metadata
Title
Position Gradient and Plane Consistency Based Feature Extraction
Authors
Sujan Chowdhury
Brijesh Verma
Ligang Zhang
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46671-2

Electronic ISBN: 978-3-319-46672-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46672-9_75

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