An Adaptive Descriptor Design for Object Recognition in the Wild

Digital images nowadays show large appearance variabilities on picture styles, in terms of color tone, contrast, vignetting, and etc. These `picture styles' are directly related to the scene radiance, image pipeline of the camera, and post processing functions (e.g., photography effect filters). Due to the complexity and nonlinearity of these factors, popular gradient-based image descriptors generally are not invariant to different picture styles, which could degrade the performance for object recognition. Given that images shared online or created by individual users are taken with a wide range of devices and may be processed by various post processing functions, to find a robust object recognition system is useful and challenging. In this paper, we investigate the influence of picture styles on object recognition by making a connection between image descriptors and a pixel mapping function g, and accordingly propose an adaptive approach based on a g-incorporated kernel descriptor and multiple kernel learning, without estimating or specifying the image styles used in training and testing. We conduct experiments on the Domain Adaptation data set, the Oxford Flower data set, and several variants of the Flower data set by introducing popular photography effects through post-processing. The results demonstrate that the proposed method consistently yields recognition improvements over standard descriptors in all studied cases.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  Z. Jane Wang,et al.  Cross-Domain Object Recognition Via Input-Output Kernel Analysis , 2013, IEEE Transactions on Image Processing.

[3]  Cristian Sminchisescu,et al.  Efficient Match Kernel between Sets of Features for Visual Recognition , 2009, NIPS.

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

[5]  Trevor Darrell,et al.  From pixels to physics: Probabilistic color de-rendering , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Matthew A. Brown,et al.  Learning Local Image Descriptors , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[9]  Haibin Ling,et al.  Deformation invariant image matching , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[11]  Stephen Lin,et al.  A New In-Camera Imaging Model for Color Computer Vision and Its Application , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Pietro Perona,et al.  Multiclass recognition and part localization with humans in the loop , 2011, 2011 International Conference on Computer Vision.

[13]  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).

[14]  Trevor Darrell,et al.  What you saw is not what you get: Domain adaptation using asymmetric kernel transforms , 2011, CVPR 2011.

[15]  Francesc Moreno-Noguer,et al.  Deformation and illumination invariant feature point descriptor , 2011, CVPR 2011.

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

[18]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Sebastian Nowozin,et al.  On feature combination for multiclass object classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[23]  Shree K. Nayar,et al.  Modeling the space of camera response functions , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Dieter Fox,et al.  Kernel Descriptors for Visual Recognition , 2010, NIPS.

[25]  Matthew A. Brown,et al.  Picking the best DAISY , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Andrew Zisserman,et al.  Descriptor Learning Using Convex Optimisation , 2012, ECCV.

[27]  Manik Varma,et al.  More generality in efficient multiple kernel learning , 2009, ICML '09.