Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism

Pooling is a ubiquitous operation in image processing algorithms that allows for higher-level processes to collect relevant low-level features from a region of interest. Currently, max-pooling is one of the most commonly used operators in the computational literature. However, it can lack robustness to outliers due to the fact that it relies merely on the peak of a function. Pooling mechanisms are also present in the primate visual cortex where neurons of higher cortical areas pool signals from lower ones. The receptive fields of these neurons have been shown to vary according to the contrast by aggregating signals over a larger region in the presence of low contrast stimuli. We hypothesise that this contrast-variant-pooling mechanism can address some of the shortcomings of max-pooling. We modelled this contrast variation through a histogram clipping in which the percentage of pooled signal is inversely proportional to the local contrast of an image. We tested our hypothesis by applying it to the phenomenon of colour constancy where a number of popular algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and Double-Opponency). For each of these methods, we investigated the consequences of replacing their original max-pooling by the proposed contrast-variant-pooling. Our experiments on three colour constancy benchmark datasets suggest that previous results can significantly improve by adopting a contrast-variant-pooling mechanism.

[1]  C. Alejandro Párraga,et al.  Feedback and Surround Modulated Boundary Detection , 2018, International Journal of Computer Vision.

[2]  Paul M. Hubel,et al.  The Perception of Color at Dawn and Dusk , 2000, CIC.

[3]  J. B. Levitt,et al.  Comparison of Spatial Summation Properties of Neurons in Macaque V1 and V2 , 2009, Journal of neurophysiology.

[4]  David A. Forsyth,et al.  A novel algorithm for color constancy , 1990, International Journal of Computer Vision.

[5]  E. Peli,et al.  Perceived contrast in complex images. , 2012, Journal of vision.

[6]  Brian V. Funt,et al.  A Large Image Database for Color Constancy Research , 2003, CIC.

[7]  Mark S. Drew,et al.  Removing Shadows from Images , 2002, ECCV.

[8]  Jonathan T. Barron,et al.  Convolutional Color Constancy , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  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).

[10]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[11]  Tomaso Poggio,et al.  Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex. , 2004, Journal of neurophysiology.

[12]  E. Reinhard Photographic Tone Reproduction for Digital Images , 2002 .

[13]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[14]  S. D. Hordley,et al.  Reevaluation of color constancy algorithm performance. , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[15]  E H Land,et al.  An alternative technique for the computation of the designator in the retinex theory of color vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Brian V. Funt,et al.  Is Machine Colour Constancy Good Enough? , 1998, ECCV.

[17]  Brian V. Funt,et al.  A data set for color research , 2002 .

[18]  Vivek Agarwal,et al.  Machine learning approach to color constancy , 2007, Neural Networks.

[19]  C. Alejandro Parraga,et al.  Colour constancy as a product of dynamic centre-surround adaptation. , 2016 .

[20]  Alessandra Angelucci,et al.  Beyond the Classical Receptive Field : Surround Modulation in Primary Visual Cortex , 2022 .

[21]  Alain Trémeau,et al.  Mixed pooling neural networks for color constancy , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[22]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[23]  Joost van de Weijer,et al.  Author Manuscript, Published in "ieee Transactions on Image Processing Edge-based Color Constancy , 2022 .

[24]  Graham D. Finlayson,et al.  Shades of Gray and Colour Constancy , 2004, CIC.

[25]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[26]  M. Carandini,et al.  Normalization as a canonical neural computation , 2013, Nature Reviews Neuroscience.

[27]  Mark S. Drew,et al.  Exemplar-Based Color Constancy and Multiple Illumination , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Brian V. Funt,et al.  Estimating Illumination Chromaticity via Support Vector Regression , 2004, Color Imaging Conference.

[29]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[30]  Javier Vazquez-Corral,et al.  Color Constancy Algorithms: Psychophysical Evaluation on a New Dataset , 2009 .

[31]  A. Akbarinia Biologically-inspired Edge Detection Through Surround Modulation , 2016 .

[32]  Mark S. Drew,et al.  The Role of Bright Pixels in Illumination Estimation , 2012, Color Imaging Conference.

[33]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[34]  G. Buchsbaum A spatial processor model for object colour perception , 1980 .

[35]  Theo Gevers,et al.  Perceptual analysis of distance measures for color constancy algorithms. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[36]  Jean Ponce,et al.  A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.

[37]  D. Foster Color constancy , 2011, Vision Research.

[38]  Naila Murray,et al.  Generalized Max Pooling , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[40]  C. Enroth-Cugell,et al.  The contrast sensitivity of retinal ganglion cells of the cat , 1966, The Journal of physiology.

[41]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[42]  Kai-Fu Yang,et al.  Color Constancy Using Double-Opponency , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  Marc Ebner,et al.  Color Constancy , 2007, Computer Vision, A Reference Guide.

[44]  Graham D. Finlayson,et al.  Reproduction Angular Error: An Improved Performance Metric for Illuminant Estimation , 2014, BMVC.

[45]  Kobus Barnard,et al.  Improvements to Gamut Mapping Colour Constancy Algorithms , 2000, ECCV.

[46]  Theo Gevers,et al.  Color Constancy Using Natural Image Statistics and Scene Semantics , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.