Laplacian affine sparse coding with tilt and orientation consistency for image classification

Recently, sparse coding has become popular for image classification. However, images are often captured under different conditions such as varied poses, scales and different camera parameters. This means local features may not be discriminative enough to cope with these variations. To solve this problem, affine transformation along with sparse coding is proposed. Although proven effective, the affine sparse coding has no constraints on the tilt and orientations as well as the encoding parameter consistency of the transformed local features. To solve these problems, we propose a Laplacian affine sparse coding algorithm which combines the tilt and orientations of affine local features as well as the dependency among local features. We add tilt and orientation smooth constraints into the objective function of sparse coding. Besides, a Laplacian regularization term is also used to characterize the encoding parameter similarity. Experimental results on several public datasets demonstrate the effectiveness of the proposed method.

[1]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Wenyu Liu,et al.  Feature context for image classification and object detection , 2011, CVPR 2011.

[3]  Li Fei-Fei,et al.  Classifying Actions and Measuring Action Similarity by Modeling the Mutual Context of Objects and Human Poses , 2011 .

[4]  Cor J. Veenman,et al.  Kernel Codebooks for Scene Categorization , 2008, ECCV.

[5]  Jean-Michel Morel,et al.  A fully affine invariant image comparison method , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Michael Isard,et al.  Bundling features for large scale partial-duplicate web image search , 2009, CVPR.

[7]  Baoxin Li,et al.  Discriminative affine sparse codes for image classification , 2011, CVPR 2011.

[8]  Xiaoqin Zhang,et al.  Use bin-ratio information for category and scene classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Qi Tian,et al.  Image classification using Harr-like transformation of local features with coding residuals , 2013, Signal Process..

[10]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Liang-Tien Chia,et al.  Local features are not lonely – Laplacian sparse coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[14]  James M. Rehg,et al.  Beyond the Euclidean distance: Creating effective visual codebooks using the Histogram Intersection Kernel , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[16]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[18]  Gang Hua,et al.  Generating Descriptive Visual Words and Visual Phrases for Large-Scale Image Applications , 2011, IEEE Transactions on Image Processing.

[19]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Fei-Fei Li,et al.  What, where and who? Classifying events by scene and object recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[21]  Qi Tian,et al.  A Boosting, Sparsity- Constrained Bilinear Model for Object Recognition , 2012, IEEE MultiMedia.

[22]  Jean-Michel Morel,et al.  ASIFT: A New Framework for Fully Affine Invariant Image Comparison , 2009, SIAM J. Imaging Sci..

[23]  Pietro Perona,et al.  A sparse object category model for efficient learning and exhaustive recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[25]  Qi Tian,et al.  Image classification by non-negative sparse coding, low-rank and sparse decomposition , 2011, CVPR 2011.

[26]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[27]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[28]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[29]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[30]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Yong Jae Lee,et al.  Object-graphs for context-aware category discovery , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.