Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization. 1. Introduction It has long been argued that texture plays a key role in computer-based pattern recognition. Texture can be the only effective way to discriminate between different surfaces that have similar spectral characteristics [1–6]. Texture was early recognized as mainly a spatial distribution of tonal variations in the same band [7]. Different grayscale texture methods have been proposed based on different techniques [7–12]. For an objective and rigorous comparison between different texture analysis methods, it is important to use standard databases [13, 14]. The standard Brodatz grayscale texture album [15] has been widely used as a validation dataset [16, 17]. It is composed of 112 grayscale images representing a large variety of natural grayscale textures. This database has been used with different levels of complexity in texture classification [18], texture segmentation [19], and image retrieval [20]. A rotation invariant version of the Brodatz database was also proposed [21] and used for texture classification and retrieval [22, 23]. Recently, we have seen a growing interest in color texture [24]. This is a natural evolution of the field of texture, from grayscale to color texture. The use of color in texture analysis showed several benefits [25–27]. In color texture, efforts have been made to find efficient methods to combine color and texture features [24]. Consequently, the evaluation of color texture methods requires images in which color and texture information are both sources of discriminative information. Many color texture databases have been proposed
References
[1]
L. R. Sarker and J. E. Nichol, “Improved forest biomass estimates using ALOS AVNIR-2 texture indices,” Remote Sensing of Environment, vol. 115, no. 4, pp. 968–977, 2011.
[2]
X. Wang, N. D. Georganas, and E. M. Petriu, “Fabric texture analysis using computer vision techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 1, pp. 44–56, 2011.
[3]
A. Kassnera and R. E. Thornhilla, “Texture analysis: a review of neurologic MR imaging applications,” American Journal of Neuroradiology, vol. 31, no. 5, pp. 809–816, 2010.
[4]
J. R. Smith, C. Y. Lin, and M. Naphade, “Video texture indexing using spatio-temporal wavelets,” in Proceedings of the International Conference on Image Processing (ICIP '02), vol. 2, pp. II/437–II/440, September 2002.
[5]
W. Phillips III, M. Shah, and N. D. Lobo, “Flame recognition in video,” Pattern Recognition Letters, vol. 23, no. 1–3, pp. 319–327, 2002.
[6]
R. C. Nelson and R. Polana, “Qualitative recognition of motion using temporal texture,” CVGIP—Image Understanding, vol. 56, no. 1, pp. 78–89, 1992.
[7]
R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973.
[8]
D. C. He and L. Wang, “Texture unit, texture spectrum, and texture analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 28, no. 4, pp. 509–512, 1990.
[9]
A. Laine and J. Fan, “Texture classification by wavelet packet signatures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1186–1191, 1993.
[10]
A. C. Bovik, M. Clark, and W. S. Geisler, “Multichannel texture analysis using localized spatial filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 55–73, 1990.
[11]
H. Xin, Z. Liangpei, and W. Le, “Evaluation of morphological texture features for mangrove forest mapping and species discrimination using multispectral IKONOS imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 3, pp. 393–397, 2009.
[12]
A. Voisin, V. A. Krylov, G. Moser, S. B. Serpico, and J. Zerubia, “classification of very high resolution SAR images of urban areas using copulas and texture in a hierarchical Markov random field model,” IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 1, pp. 96–100, 2013.
[13]
R. M. Haralick, “Performance characterization in computer vision,” CVGIP—Image Understanding, vol. 60, no. 2, pp. 245–249, 1994.
[14]
P. J. Phillips and K. W. Bowyer, “Empirical evaluation of computer vision algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 289–290, 1999.
[15]
P. Brodatz, Testures: A Photographic Album for Artists & Designers, Dover, New York, NY, USA, 1966.
[16]
L. Liu and P. Fieguth, “Texture classification from random features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 574–586, 2012.
[17]
J. Yang, Y. Zhuang, and F. Wu, “ESVC-based extraction and segmentation of texture features,” Computers & Geosciences, vol. 49, pp. 238–247, 2012.
[18]
F. M. Khellah, “Texture classification using dominant neighborhood structure,” IEEE Transactions on Image Processing, vol. 20, no. 11, pp. 3270–3279, 2011.
[19]
B. M. Carvalho, T. S. Souza, and E. Garduno, “Texture fuzzy segmentation using adaptive affinity functions,” in Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 51–53, Trento, Italy, March 2012.
[20]
I. J. Sumana, G. Lu, and D. Zhang, “Comparison of curvelet and wavelet texture features for content based image retrieval,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME '12), pp. 290–295, July 2012.
[21]
T. Ojala, T. Maenpaa, M. Pietikainen, J. Viertola, J. Kyllonen, and S. Huovinen, “Outex–new framework for empirical evaluation of texture analysis algorithms,” in Proceedings of the 16th International Conference on Pattern Recognition (ICPR '02), vol. 1, pp. 701–706, August 2002.
[22]
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
[23]
P. Janney and Z. Yu, “Invariant features of local textures—a rotation invariant local texture descriptor,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), June 2007.
[24]
D. E. Ilea and P. F. Whelan, “Image segmentation based on the integration of colourtexture descriptors—a review,” Pattern Recognition, vol. 44, no. 10-11, pp. 2479–2501, 2011.
[25]
Y. Deng and B. S. Manjunath, “Unsupervised segmentation of color-texture regions in images and video,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 8, pp. 800–810, 2001.
[26]
G. Paschos, “Perceptually uniform color spaces for color texture analysis: an empirical evaluation,” IEEE Transactions on Image Processing, vol. 10, no. 6, pp. 932–937, 2001.
[27]
F. Bianconi, A. Fernández, E. González, D. Caride, and A. Calvi？o, “Rotation-invariant colour texture classification through multilayer CCR,” Pattern Recognition Letters, vol. 30, no. 8, pp. 765–773, 2009.
[28]
K. J. Dana, B. van Ginneken, S. K. Nayar, and J. J. Koenderink, “Reflectance and texture of real-world surfaces,” ACM Transactions on Graphics (TOG), vol. 18, no. 1, pp. 1–34, 1999.
[29]
L. Wang and D. He, “A new statistical approach for texture analysis,” PE&RS, vol. 56, no. 1, pp. 61247–61266, 1990.
[30]
H. Weschsler and M. Kidode, “A random walk procedure for texture discrimination,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 3, pp. 272–280, 1979.
[31]
L. M. Kaplan, “Extended fractal analysis for texture classification and segmentation,” IEEE Transactions on Image Processing, vol. 8, no. 11, pp. 1572–1585, 1999.
[32]
J. Melendez, M. A. Garcia, D. Puig, and M. Petrou, “Unsupervised texture-based image segmentation through pattern discovery,” Computer Vision and Image Understanding, vol. 115, no. 8, pp. 1121–1133, 2011.
[33]
K. I. Kilic and R. H. Abiyev, “Exploiting the synergy between fractal dimension and lacunarity for improved texture recognition,” Signal Processing, vol. 91, no. 10, pp. 2332–2344, 2011.
[34]
K. Zuiderveld, “Contrast limited adaptive histogram equalization,” in Graphics Gems IV, pp. 474–485, Morgan Kaufmann, Burlington, Mass, USA, 1994.
[35]
Digital Image Processing, R. C. Gonzalez and R. E. Woods Eds., Prentice Hall, Upper Saddle River, NJ, USA, 2008.
[36]
A. Drimbarean and P. F. Whelan, “Experiments in colour texture analysis,” Pattern Recognition Letters, vol. 22, no. 10, pp. 1161–1167, 2001.
[37]
A. Rosenfeld, C. Y. Wang, and A. Y. Wu, “Multispectral texture,” IEEE Transactions on Systems, Man and Cybernetics, vol. 12, no. 1, pp. 79–84, 1982.
[38]
A. Stolpmann and L. S. Dooley, “Genetic algorithms for automized feature selection in a texture classification system,” in Proceedings of the 4th International Conference on Signal Processing Proceedings (ICSP '98), vol. 2, pp. 1229–1232, October 1998.
[39]
J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles, vol. 7, Image Rochester, New York, NY, USA, 1974.
[40]
D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002.
[41]
M. Unser, “Texture classification and segmentation using wavelet frames,” IEEE Transactions on Image Processing, vol. 4, no. 11, pp. 1549–1560, 1995.
[42]
Y. Dong and J. Ma, “Wavelet-based image texture classification using local energy histograms,” IEEE Signal Processing Letters, vol. 18, no. 4, pp. 247–250, 2011.
[43]
M. Acharyya, R. K. De, and M. K. Kundu, “Extraction of features using M-band wavelet packet frame and their neuro-fuzzy evaluation for multitexture segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1639–1644, 2003.
[44]
A. Safia, M. F. Belbachir, and T. Iftene, “A wavelet transformation for combining texture and color: application to the combined classification of the HRV SPOT images,” International Journal of Remote Sensing, vol. 27, no. 18, pp. 3977–3990, 2006.
[45]
G. van de Wouwer, P. Scheunders, S. Livens, and D. van Dyck, “Wavelet correlation signatures for color texture characterization,” Pattern Recognition, vol. 32, no. 3, pp. 443–451, 1999.
[46]
A. Mojsilovic, D. Rackov, and M. Popovic, “On the selection of an optimal wavelet basis for texture characterization,” in Proceedings of the International Conference on Image Processing (ICIP '98), vol. 3, pp. 678–682, October 1998.
[47]
G. Paschos and K. P. Valavanis, “A color texture based visual monitoring system for automated surveillance,” IEEE Transactions on Systems, Man and Cybernetics C, vol. 29, no. 2, pp. 298–307, 1999.
[48]
C. Garcia and G. Tziritas, “Face detection using quantized skin color regions merging and wavelet packet analysis,” IEEE Transactions on Multimedia, vol. 1, no. 3, pp. 264–277, 1999.
[49]
J. Chen, T. N. Pappas, A. Mojsilovi？, and B. E. Rogowitz, “Adaptive perceptual color-texture image segmentation,” IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1524–1536, 2005.
[50]
X. Y. Wang, T. Wang, and J. Bu, “Color image segmentation using pixel wise support vector machine classification,” Pattern Recognition, vol. 44, no. 4, pp. 777–787, 2011.
[51]
A. Sengur, “Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification,” Expert Systems with Applications, vol. 34, no. 3, pp. 2120–2128, 2008.
[52]
A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry, “Unsupervised segmentation of natural images via lossy data compression,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 212–225, 2008.
[53]
A. Emran, M. Hakdaoui, and J. Chorowicz, “Anomalies on geologic maps from multispectral and textural classification: the bleida mining district (Morocco),” Remote Sensing of Environment, vol. 57, no. 1, pp. 13–21, 1996.
