基于 GaborSIFT+NNScSPM 图像特征抽取算法研究

江爱文; 王春恒; 肖柏华

doi:10.3724/SP.J.1004.2011.01183

基于 GaborSIFT+NNScSPM 图像特征抽取算法研究

doi: 10.3724/SP.J.1004.2011.01183

1.
江西师范大学计算机与信息工程学院南昌 330022;
2.
中国科学院自动化研究所复杂系统智能控制与管理国家重点实验室(筹) 北京 100190

详细信息

通讯作者:
江爱文江西师范大学计算机与信息工程学院讲师. 2010年获中国科学院自动化研究所博士学位. 主要研究方向为图像处理与模式识别. E-mail: aiwen.jiang@ia.ac.cn

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出版历程
- 收稿日期: 2010-09-15
- 修回日期: 2011-05-17
- 刊出日期: 2011-10-20

An Image Feature Extraction Method Based on GaborSIFT+NNScSPM

1.
College of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022;
2.
State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

摘要

摘要: 视觉信息的特征表示是计算机视觉场景图像理解研究中的核心内容. 基于GaborSIFT+NNScSPM的图像特征抽取算法,借鉴生物视觉机制中的相关研究成果,有机结合了HMAX层次计算模型的思想和非负稀疏编码的策略, 较为合理地模拟了生物视觉皮层中视觉处理的过程.在15类场景图像和Caltech101两个公开数据集上进行了实验验证, 实验结果表明我们所提出的算法较同期算法有着良好的分类性能.
- 特征抽取 /
- 生物视觉机制 /
- HMAX /
- 非负稀疏编码 /
- 语义分类
Abstract: Feature representation of visual information is one of core research topics in computer vision and image understanding. In this paper, we propose a feature extraction method based on GaborSIFT+NNScSPM, trying to combine HMAX model with non-negative sparse coding to mimic the information process in V1 area in visual cortex. We have test our proposed method on two public data sets (15 scenes and Caltech101), and the experiment results show that our method outperforms the existing ones.
- Feature extraction /
- biological inspired feature /
- HMAX /
- non-negative sparse coding /
- semantic classification

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参考文献(1)

[1]

Vailaya A, Figueiredo M A T, Jain A K, Zhang H J. Image classification for content-based indexing. IEEE Transactions on Image Processing, 2001, 10(1): 117-130[2] Chang E, Goh K, Sychay G, Wu G. CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(1): 26-38[3] Szummer M, Picard R W. Indoor-outdoor image classification. In: Proceedings of International Workshop on Content-Based Access of Image and Video Database. Bombay, India: IEEE. 1998. 42-51[4] Serrano N, Savakis A E, Luo J B. Improved scene classification using efficient low-level features and semantic cues. Pattern Recognition, 2004, 37(9): 1773-1784[5] Fan J P, Gao Y L, Luo H Z, Xu G Y. Statistical modeling and conceptualization of natural images. Pattern Recognition, 2005, 38(6): 865-885[6] Luo J B, Savakis A E, Singhal A. A Bayesian network-based framework for semantic image understanding. Pattern Recognition, 2005, 38(6): 919-934[7] Vogel J, Schiele B. Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 2007, 72(2): 133-157[8] Li F F, Perona P. A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA: IEEE. 2005. 524-531[9] Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, NewYork, USA: IEEE, 2006. 2169-2178[10] Quelhas P, Monay F, Odobez J M, Gatica-Perez D, Tuytelaars T. A thousand words in a scene. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(9): 1575-1589[11] 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[12] Hubel D H, Wiesel T N. Receptive fields of single neurones in the cat's striate cortex. The Journal of Physiology, 1959, 148(3): 574-591[13] Oliva A, Torralba A. Building the gist of a scene: the role of global image features in recognition. Progress in Brain Research, 2006, 155(1): 23-36[14] Li F F, Van Rullen R, Koch C, Perona P. Rapid natural scene categorization in the near absence of attention. Proceedings of the National Academy of Sciences, 2002, 99(14): 9596-9601[15] Poggio T, Riesenhuber M. Hierarchical models of object recognition in cortex. Nature Neuroscience, 1999, 2(11): 1019-1025[16] Serre T, Wolf T, Bileschi S, Riesenhuber X, Poggio T. Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(3): 411-426[17] Mutch J, Lowe D G. Object class recognition and localization using sparse features with limited receptive fields. International Journal of Computer Vision, 2008, 80(1): 45-57[18] Olshausen B A, Field D J. How close are we to understanding V1? Neural Computation, 2005, 17(8): 1665-1699[19] Carandini M, Demb J B, Mante V, Toullhurst D J, Dan Y, Olshausen B A, Gallant J L, Rust N C. Do we know what the early visual system does? Journal of Neuroscience, 2005, 25(46): 10577-10597[20] Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 1996, 381(6583): 607-609[21] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401(6755): 788-791[22] Hoyer P O. Modeling receptive fields with non-negative sparse coding. Neurocomputing, 2003, 52-54(1): 547-552[23] Yang J C, Yu K, Gong Y H, Huang T. Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, USA: IEEE. 2009. 1794-1801[24] Jiang A W, Wang C H, Xiao B H. A new biologically inspired feature for scene image classification. In: Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey: IEEE, 2010. 758-761[25] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110

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