Abstract：The local relationship between features of images is not taken into account in the traditional sparse coding, it can lead to the instability of encoding. Moreover, some effective features may not be retained via the subtraction operation in the optimization procedure. Aiming at these two problems, a method is proposed, named Laplacian sparse coding by incorporating locality and non-negativity(LN-LSC)for image classification. Firstly, bases near to the local features are chosen to constrain the codes. Then, non-negativity is introduced in Laplacian sparse coding by non-negative matrix factorization. Finally, spatial pyramid division and max pooling are utilized to generate the final representation of images in the pooling step. Multi-class linear SVM is adopted for image classification. The local information between features is preserved by the proposed method, and the offsetting between features is also avoided. Thus, more features are utilized for coding, and the instability of the coding is overcome. Experiments on four public image datasets show the classification accuracy of LN-LSC is higher than that of the state-of-the-art sparse coding algorithms.
[1] 冀 中,聂林红.基于抗噪声局部二值模式的纹理图像分类.计算机研究与发展, 2016, 53(5): 1128-1135. (JI Z, NIE L H. Texture Image Classification with Noise-Tolerant Local Binary Pattern. Journal of Computer Research and Development, 2016, 53(5): 1128-1135.) [2] 郭立君,刘 曦,赵杰煜,等.基于改进局部特征分布的图像分类方法.模式识别与人工智能, 2011, 24(3): 368-375. (GUO L J, LIU X, ZHAO J Y, et al. Efficient Image Categorization Based on Improved Distributions of Local Features. Pattern Recognition and Artificial Intelligence, 2011, 24(3): 368-375.) [3] LOWE D G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110. [4] YANG J C, YU K, GONG Y H, et al. Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2009: 1794-1801. [5] GAO S H, TSANG I W H, CHIA L T, et al. Local Features Are Not Lonely-Laplacian Sparse Coding for Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2010: 3555-3561. [6] WANG J J, YANG J C, YU K, et al. Locality-Constrained Linear Coding for Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010: 3360-3367. [7] MIN H Q, LIANG M J, LUO R H, et al. Laplacian Regularized Locality-Constrained Coding for Image Classification. Neurocomputing, 2015, 171: 1486-1495. [8] 刘培娜,刘国军,郭茂祖,等.非负局部约束线性编码图像分类算法.自动化学报, 2015, 41(7): 1235-1243. (LIU P N, LIU G J, GUO M Z, et al. Image Classification Based on Non-negative Locality-Constrained Linear Coding. Acta Automa-tica Sinica, 2015, 41(7): 1235-1243.) [9] MUKHERJEE L, HALL A. Non-negative Sparse Coding with Regularizer for Image Classification // Proc of the IEEE Winter Confe-rence on Applications of Computer Vision. Washington, USA: IEEE, 2015: 852-859. [10] LEE D D, SEUNG H S. Learning the Parts of Objects by Nonnegative Matrix Factorization. Nature, 1999, 401: 788-791. [11] LEE D D, SEUNG H S. Algorithms for Non-negative Matrix Factorization // LEEN T K, DIETTERICH T G, TRESP V, eds. Advances in Neural Information Processing Systems 13. Cambridge, USA: The MIT Press, 2000: 556-562. [12] HAN H, LIU S J, GAN H. Non-negativity and Dependence Constrained Sparse Coding for Image Classification. Journal of Visual Communication and Image Representation, 2015, 26: 247-254. [13] SIVIC J, ZISSERMAN A. Video Google: A Text Retrieval Approach to Object Matching in Videos // Proc of the International Conference on Computer Vision. Washington, USA: IEEE, 2003, II: 1470-1477. [14] LAZEBNIK S, SCHMID C, PONCE J. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2006, II: 2169-2178. [15] YU K, ZHANG T, GONG Y H. Nonlinear Learning Using Local Coordinate Coding // BENGIO Y, SCHUURMANS D, LAFFERTY J D, et al., eds. Advances in Neural Information Processing Systems 22. Cambridge, USA: The MIT Press, 2009: 2223-2231. [16] WU J X, REHG J M. Beyond the Euclidean Distance: Creating Effective Visual Codebooks Using the Histogram Intersection Kernel // Proc of the 12th IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2009: 630-637. [17] LEE H, BATTLE A, RAINA R, et al. Efficient Sparse Coding Algorithms // SCHLKOPF P B, PLATT J C, HAFFMAN T, eds. Advances in Neural Information Processing Systems 19. Cambridge, USA: The MIT Press, 2006: 801-808. [18] LU Z W, IP H H S. Image Categorization with Spatial Mismatch Kernels // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2009: 397-404. [19] LI F F, PERONA P. A Bayesian Hierarchical Model for Learning Natural Scene Categories // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005, II: 524-531. [20] LI F F, FERGUS R, PERONA P. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. Computer Vision and Image Understanding, 2007, 106(1): 59-70. [21] GRIFFIN G, HOLUB A, PERONA P. Caltech-256 Object Category Dataset. Technical Report, CNS-TR-2007-001. Pasadena, USA: California Institute of Technology, 2007. [22] GAO S H, TSANG I W H, CHIA L T. Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 92-104. [23] OLSHAUSEN B A, FIELD D J. Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images. Nature, 1996, 381: 607-609.
2017, Vol. 30 
