Outex - new framework for empirical evaluation of texture analysis algorithms. This paper presents the current status of a new initiative aimed at developing a versatile framework and image database for empirical evaluation of texture analysis algorithms. The proposed Outex framework contains a large collection of surface textures captured under different conditions, which facilitates construction of a wide range of texture analysis problems. The problems are encapsulated into test suites, for which baseline results obtained with algorithms from literature are provided. The rich functionality of the framework is demonstrated with examples in texture classification, segmentation and retrieval. The framework has a web site for public dissemination of the database and comparative results obtained by research groups world wide.

References in zbMATH (referenced in 33 articles )

Showing results 1 to 20 of 33.
Sorted by year (citations)

1 2 next

  1. Chung, Yu-Min; Lawson, Austin: Persistence curves: a canonical framework for summarizing persistence diagrams (2022)
  2. Saito, Naoki; Shao, Yiqun: eGHWT: the extended generalized Haar-Walsh transform (2022)
  3. Hijazi, Samah; Hamad, Denis; Kalakech, Mariam; Kalakech, Ali: Active learning of constraints for weighted feature selection (2021)
  4. de Mesquita Sá Junior, Jarbas Joaci; Backes, André Ricardo; Bruno, Odemir Martinez: Randomized neural network based signature for color texture classification (2019)
  5. He, Bo; Song, Yan; Zhu, Yuemei; Sha, Qixin; Shen, Yue; Yan, Tianhong; Nian, Rui; Lendasse, Amaury: Local receptive fields based extreme learning machine with hybrid filter kernels for image classification (2019)
  6. Yuan, Feiniu; Xia, Xue; Shi, Jinting: Holistic learning-based high-order feature descriptor for smoke recognition (2019)
  7. Ringh, Axel; Karlsson, Johan; Lindquist, Anders: Multidimensional rational covariance extension with approximate covariance matching (2018)
  8. Aptoula, Erchan; Pham, Minh-Tan; Lefèvre, Sébastien: Quasi-flat zones for angular data simplification (2017)
  9. Florindo, João Batista; Bruno, Odemir Martinez: Discrete Schroedinger transform for texture recognition (2017)
  10. Oliveira, Marcos William da Silva; da Silva, Núbia Rosa; Manzanera, Antoine; Bruno, Odemir Martinez: Feature extraction on local jet space for texture classification (2015)
  11. Căliman, Alexandru; Ivanovici, Mihai; Richard, Noël: Probabilistic pseudo-morphology for grayscale and color images (2014) ioport
  12. Guo, Yimo; Zhao, Guoying; Pietikäinen, Matti: Local configuration features and discriminative learnt features for texture description (2014) ioport
  13. Fernández, Antonio; Álvarez, Marcos X.; Bianconi, Francesco: Texture description through histograms of equivalent patterns (2013)
  14. Maani, Rouzbeh; Kalra, Sanjay; Yang, Yee-Hong: Noise robust rotation invariant features for texture classification (2013) ioport
  15. Porebski, A.; Vandenbroucke, N.; Macaire, L.: Supervised texture classification: color space or texture feature selection? (2013) ioport
  16. Zhu, Chao; Bichot, Charles-Edmond; Chen, Liming: Image region description using orthogonal combination of local binary patterns enhanced with color information (2013) ioport
  17. Alvarez, Susana; Vanrell, Maria: Texton theory revisited: a bag-of-words approach to combine textons (2012) ioport
  18. Aptoula, Erchan: Extending morphological covariance (2012) ioport
  19. Guo, Yimo; Zhao, Guoying; Pietikäinen, Matti: Discriminative features for texture description (2012) ioport
  20. Zhu, Changren; Wang, Runsheng: Local multiple patterns based multiresolution gray-scale and rotation invariant texture classification (2012) ioport

1 2 next