Covariance of Covariance Features for Image Classification

In this paper we propose a novel image descriptor built by computing the covariance of pixel level features on densely sampled patches and encoding them using their covariance. Appropriate projections to the Euclidean space and feature normalizations are employed in order to provide a strong descriptor usable with linear classifiers. In order to remove border effects, we further enhance the Spatial Pyramid representation with bilinear interpolation. Experimental results conducted on two common datasets for object and texture classification show that the performance of our method is comparable with state of the art techniques, but removing any dataset specific dependency in the feature encoding step.

[1]  Fatih Murat Porikli,et al.  Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Andrew Zisserman,et al.  Efficient Additive Kernels via Explicit Feature Maps , 2012, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Chong Wang,et al.  Exploring relations of visual codes for image classification , 2011, CVPR 2011.

[4]  Mario Fritz,et al.  On the Significance of Real-World Conditions for Material Classification , 2004, ECCV.

[5]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[6]  Cordelia Schmid,et al.  A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.

[8]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[11]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Andrew Zisserman,et al.  The devil is in the details: an evaluation of recent feature encoding methods , 2011, BMVC.

[13]  Larry S. Davis,et al.  Submodular dictionary learning for sparse coding , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  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).

[15]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[16]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[17]  Trevor Darrell,et al.  The NBNN kernel , 2011, 2011 International Conference on Computer Vision.

[18]  Bingbing Ni,et al.  Geometric ℓp-norm feature pooling for image classification , 2011, CVPR 2011.

[19]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[20]  Donghui Wang,et al.  A Dictionary Learning Approach for Classification: Separating the Particularity and the Commonality , 2012, ECCV.

[21]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[22]  Qilong Wang,et al.  Local Log-Euclidean Covariance Matrix (L2ECM) for Image Representation and Its Applications , 2012, ECCV.

[23]  Vittorio Murino,et al.  Characterizing Humans on Riemannian Manifolds , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, CVPR Workshops.