A Robust Descriptor for Color Texture Classification Under Varying Illumination

Classifying color textures under varying illumination sources remains challenging. To address this issue, this paper introduces a new descriptor for color texture classification, which is robust to changes in the scene illumination. The proposed descriptor, named Color Intensity Local Mapped Pattern (CILMP), incorporates relevant information about the color and texture patterns from the image in a multiresolution fashion. The CILMP descriptor explores the color features by comparing the magnitude of the color vectors inside the RGB cube. The proposed descriptor is evaluated on nine experiments over 50,048 images of raw food textures acquired under 46 lighting conditions. The experimental results have shown that CILMP performs better than the state-of-the-art methods, reporting an increase (up to 20.79%) in the classification accuracy, compared to the second-best descriptor. In addition, we concluded from the experimental results that the multiresolution analysis improves the robustness of the descriptor and increases the classification accuracy.

[1]  Paolo Napoletano,et al.  Illuminant Invariant Descriptors for Color Texture Classification , 2013, CCIW.

[2]  Paul F. Whelan,et al.  Experiments in colour texture analysis , 2001, Pattern Recognit. Lett..

[3]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Chi-Ho Chan,et al.  Multispectral Local Binary Pattern Histogram for Component-based Color Face Verification , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[5]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[6]  Matti Pietikäinen,et al.  Classification with color and texture: jointly or separately? , 2004, Pattern Recognit..

[7]  B. Wandell,et al.  Pattern—color separable pathways predict sensitivity to simple colored patterns , 1996, Vision Research.

[8]  Paolo Napoletano,et al.  Evaluating color texture descriptors under large variations of controlled lighting conditions , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[9]  Bt Thomas,et al.  Segmentation of natural images using self-organising feature maps , 1996 .

[10]  Georgios S. Paschos,et al.  Fast color texture recognition using chromaticity moments , 2000, Pattern Recognit. Lett..

[11]  Donald A. Adjeroh,et al.  Illumination-Invariant Morphological Texture Classification , 2005, ISMM.

[12]  Shamik Sural,et al.  An Integrated Color and Intensity Co-occurrence Matrix , 2007, Pattern Recognit. Lett..

[13]  A. Benassi,et al.  GENERALIZATION OF THE COOCCURRENCE MATRIX FOR COLOUR IMAGES: APPLICATION TO COLOUR TEXTURE CLASSIFICATION , 2011 .

[14]  Donald A. Adjeroh,et al.  Robust Color Texture Features Under Varying Illumination Conditions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Abdulkadir Sengür,et al.  Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification , 2008, Expert Syst. Appl..

[16]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[17]  C. Palm,et al.  Classification of color textures by Gabor filtering , 2002 .

[18]  Adilson Gonzaga,et al.  Feature description based on center-symmetric local mapped patterns , 2014, SAC.

[19]  Paolo Napoletano,et al.  Combining local binary patterns and local color contrast for texture classification under varying illumination. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[20]  Urbano Nunes,et al.  Trainable classifier-fusion schemes: An application to pedestrian detection , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[21]  Christoph Palm,et al.  Color texture classification by integrative Co-occurrence matrices , 2004, Pattern Recognit..

[22]  Paul Scheunders,et al.  Wavelet correlation signatures for color texture characterization , 1999, Pattern Recognit..

[23]  Amit Jain,et al.  A multiscale representation including opponent color features for texture recognition , 1998, IEEE Trans. Image Process..

[24]  Paul Southam,et al.  Theoretical and experimental comparison of different approaches for color texture classification , 2011, J. Electronic Imaging.

[25]  Neill W Campbell,et al.  Using Colour Gabor Texture Features for Scene Understanding , 1999 .

[26]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..