Domain Adaptation Image Classification Based on Multi-sparse Representation

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.

[1]  Jing Liu,et al.  Robust Structured Subspace Learning for Data Representation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  S. Shan,et al.  Domain Adaptation for Face Recognition: Targetize Source Domain Bridged by Common Subspace , 2014, International Journal of Computer Vision.

[3]  Xuelong Li,et al.  Image Annotation by Multiple-Instance Learning With Discriminative Feature Mapping and Selection , 2014, IEEE Transactions on Cybernetics.

[4]  Barbara Caputo,et al.  Frustratingly Easy NBNN Domain Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[5]  Rama Chellappa,et al.  Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Cewu Lu,et al.  Online Robust Dictionary Learning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Rama Chellappa,et al.  Remote identification of faces: Problems, prospects, and progress , 2012, Pattern Recognit. Lett..

[8]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Dong Liu,et al.  Robust visual domain adaptation with low-rank reconstruction , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Meng Wang,et al.  Oracle in Image Search: A Content-Based Approach to Performance Prediction , 2012, TOIS.

[11]  Hossein Mobahi,et al.  Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Rama Chellappa,et al.  Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.

[13]  Meng Wang,et al.  Multimedia answering: enriching text QA with media information , 2011, SIGIR.

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

[15]  Xiaogang Wang,et al.  Background Subtraction via Robust Dictionary Learning , 2011, EURASIP J. Image Video Process..

[16]  Lorenzo Torresani,et al.  Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach , 2010, NIPS.

[17]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[18]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.

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

[20]  Koby Crammer,et al.  A theory of learning from different domains , 2010, Machine Learning.

[21]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[22]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

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

[24]  Lorenzo Bruzzone,et al.  Toward the Automatic Updating of Land-Cover Maps by a Domain-Adaptation SVM Classifier and a Circular Validation Strategy , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Qiang Yang,et al.  Transfer Learning via Dimensionality Reduction , 2008, AAAI.

[26]  Eli Shechtman,et al.  In defense of Nearest-Neighbor based image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Rong Yan,et al.  Cross-domain video concept detection using adaptive svms , 2007, ACM Multimedia.

[28]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[29]  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).

[30]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[31]  J. Heckman.  Sample selection bias as a specification error , 1979 .