Reflection Invariance: an important consideration of image orientation

In this position paper, we consider the state of computer vision research with respect to invariance to the horizontal orientation of an image -- what we term reflection invariance. We describe why we consider reflection invariance to be an important property and provide evidence where the absence of this invariance produces surprising inconsistencies in state-of-the-art systems. We demonstrate inconsistencies in methods of object detection and scene classification when they are presented with images and the horizontal mirror of those images. Finally, we examine where some of the invariance is exhibited in feature detection and descriptors, and make a case for future consideration of reflection invariance as a measure of quality in computer vision algorithms.

[1]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[2]  Xiaochun Cao,et al.  MIFT: A framework for feature descriptors to be mirror reflection invariant , 2012, Image Vis. Comput..

[3]  Krista A. Ehinger,et al.  SUN Database: Exploring a Large Collection of Scene Categories , 2014, International Journal of Computer Vision.

[4]  Jason Yosinski,et al.  Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[6]  David G. Lowe,et al.  Shape Descriptors for Maximally Stable Extremal Regions , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[8]  Ioannis Patras,et al.  Mirror, mirror on the wall, tell me, is the error small? , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[11]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[12]  Luc Van Gool,et al.  Hough Forests for Object Detection, Tracking, and Action Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[14]  Pushmeet Kohli,et al.  On Detection of Multiple Object Instances Using Hough Transforms , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Yiannis Kompatsiaris,et al.  Proceedings of the ACM International Conference on Image and Video Retrieval , 2009, CIVR 2009.

[16]  Juergen Gall,et al.  Class-specific Hough forests for object detection , 2009, CVPR.

[17]  Tatsuya Harada,et al.  Mirror reflection invariant HOG descriptors for object detection , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[18]  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).

[19]  Kai Ma,et al.  Compound Exemplar Based Object Detection by Incremental Random Forest , 2014, 2014 22nd International Conference on Pattern Recognition.