Minimizing dataset bias: Discriminative multi-task sparse coding through shared subspace learning for image classification

Sparse coding was shown to be able to find succinct representations of stimuli. Recently, it has been successfully applied to a variety of problems in image processing analysis. Sparse coding models data vectors as a linear combination of a few elements from a dictionary. However, most existing sparse coding methods are applied for a single task on a single dataset. The learned dictionary is then possibly biased towards the specific dataset and lacks of generalization abilities. In light of this, in this paper we propose a multitask sparse coding approach by uncovering a shared subspace among heterogeneous datasets. The proposed multi-task coding strategy leverages the commonality benefit from different datasets. Moreover, our multi-task coding framework is capable of direct classification by incorporating label information. Experimental results show that the dictionary learned by our approach has more generalization abilities and our model performs better classification compared to the model learned from only one dataset or the model learned from simply pooling different datasets together.

[1]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[2]  Nicu Sebe,et al.  Web Image Annotation Via Subspace-Sparsity Collaborated Feature Selection , 2012, IEEE Transactions on Multimedia.

[3]  Baoxin Li,et al.  Discriminative K-SVD for dictionary learning in face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[5]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[6]  Massimiliano Pontil,et al.  Sparse coding for multitask and transfer learning , 2012, ICML.

[7]  Francesco G. B. De Natale,et al.  Learning and matching human activities using regular expressions , 2010, 2010 IEEE International Conference on Image Processing.

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

[9]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[10]  Massimiliano Pontil,et al.  Multi-Task Feature Learning , 2006, NIPS.

[11]  Zi Huang,et al.  Multiple feature hashing for real-time large scale near-duplicate video retrieval , 2011, ACM Multimedia.

[12]  Nicu Sebe,et al.  GLocal tells you more: Coupling GLocal structural for feature selection with sparsity for image and video classification , 2014, Comput. Vis. Image Underst..

[13]  Nicu Sebe,et al.  Multi-task linear discriminant analysis for multi-view action recognition , 2013, 2013 IEEE International Conference on Image Processing.

[14]  Bo Zhang,et al.  Recognition of social interactions based on feature selection from visual codebooks , 2013, 2013 IEEE International Conference on Image Processing.

[15]  Guillermo Sapiro,et al.  Discriminative learned dictionaries for local image analysis , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Subramanian Ramanathan,et al.  No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion , 2013, 2013 IEEE International Conference on Computer Vision.

[17]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[18]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Zi Huang,et al.  Inter-media hashing for large-scale retrieval from heterogeneous data sources , 2013, SIGMOD '13.

[20]  Subramanian Ramanathan,et al.  Active transfer learning for multi-view head-pose classification , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[21]  Guillermo Sapiro,et al.  Online dictionary learning for sparse coding , 2009, ICML '09.