On the Significance of Real-World Conditions for Material Classification

Classifying materials from their appearance is a challenging problem, especially if illumination and pose conditions are permitted to change: highlights and shadows caused by 3D structure can radically alter a sample’s visual texture. Despite these difficulties, researchers have demonstrated impressive results on the CUReT database which contains many images of 61 materials under different conditions. A first contribution of this paper is to further advance the state-of-the-art by applying Support Vector Machines to this problem. To our knowledge, we record the best results to date on the CUReT database.

[1]  N. S. Barnett,et al.  Private communication , 1969 .

[2]  B. Julesz.  Textons, the elements of texture perception, and their interactions , 1981, Nature.

[3]  Zhigang Fan,et al.  Rotation and scale invariant texture classification , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[4]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[6]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[8]  Leszek Wojnar,et al.  Image Analysis , 1998 .

[9]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.

[10]  Nello Cristianini,et al.  Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[11]  Vapnik,et al.  SVMs for Histogram Based Image Classification , 1999 .

[12]  Jitendra Malik,et al.  Textons, contours and regions: cue integration in image segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[14]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[15]  Jan-Olof Eklundh,et al.  A pure learning approach to background-invariant object recognition using pedagogical support vector learning , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[16]  Bernhard Schölkopf,et al.  Kernel machine based learning for multi-view face detection and pose estimation , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  Kristin J. Dana,et al.  Compact representation of bidirectional texture functions , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[18]  Sameer Singh,et al.  Nearest-neighbour classifiers in natural scene analysis , 2001, Pattern Recognit..

[19]  Hang Joon Kim,et al.  Support Vector Machines for Texture Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[21]  Andrew Zisserman,et al.  Classifying Images of Materials: Achieving Viewpoint and Illumination Independence , 2002, ECCV.

[22]  Barbara Caputo,et al.  How to Combine Color and Shape Information for 3D Object Recognition: Kernels do the Trick , 2002, NIPS.

[23]  Maria Petrou,et al.  Illuminant Rotation Invariant Classification of 3D Surface Textures using Lissajou's Ellepses , 2002 .

[24]  Jitendra Malik,et al.  Spectral Partitioning with Indefinite Kernels Using the Nyström Extension , 2002, ECCV.

[25]  Francesca Odone,et al.  Hausdorff Kernel for 3D Object Acquisition and Detection , 2002, ECCV.

[26]  Cordelia Schmid,et al.  Affine-invariant local descriptors and neighborhood statistics for texture recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[27]  Matti Pietikäinen,et al.  Multi-scale Binary Patterns for Texture Analysis , 2003, SCIA.

[28]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[29]  Shai Avidan,et al.  Support vector tracking , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[31]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[32]  Mario Fritz,et al.  THE KTH-TIPS database , 2004 .

[33]  Sung Yong Shin,et al.  On pixel-based texture synthesis by non-parametric sampling , 2006, Comput. Graph..