A Granulometry Based Descriptor for Object Categorization

  • Arnaldo Câmara Lara
  • Roberto HirataJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7883)


The progress in the area of object recognition in the last decade is impressive. The literature reports new descriptors, new strategies, new ways to combine descriptors and classifiers and new problems in a so fast pace that it is hard to follow the whole area. A recent problem in the area is the fine-grained categorization. In this work, to address this problem, we propose a descriptor based on the application of morphological granulometries in the map of edges of an image. This descriptor is used to characterize the distribution of lengths and orientations of edges and to build a model for generic objects. We also propose a new spatial quantization with an arbitrary number of levels and divisions in each level. This quantization is so flexible that adjacent regions may have overlapping areas to avoid breakages in the structures that are near the border of the regions as it happens in the traditional spatial pyramids. Both approaches are used in a challenging and recent object recognition problem, the categorization of very similar classes. The proposed descriptor was used along with other descriptors and the overall performance of our solution to this problem was about 8% better than other work using the bag-of-words approach reported in the literature. Our descriptor showed a result 12% better when compared to the results of other edge-related descriptor in the categorization of very similar classes.


Granulometry Application Spatial Quantization Descriptor Edges Object Categorization 


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  1. 1.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: CVPR, New York, pp. 2169–2178 (2006)Google Scholar
  2. 2.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR, San Francisco, pp. 3360–3367 (2010)Google Scholar
  3. 3.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 110, 346–359 (2008)CrossRefGoogle Scholar
  4. 4.
    Selim, S., Ismail, M.: K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality. IEEE Trans. on PAMI 6, 81–87 (1984)zbMATHCrossRefGoogle Scholar
  5. 5.
    Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proc. ACM Int. Conf. on Image and Video Retrieval, pp. 401–408 (2007)Google Scholar
  6. 6.
    Matheron, G.: Random sets and integral geometry, vol. 261. Wiley, New York (1975)zbMATHGoogle Scholar
  7. 7.
    Newell III., J.: Pixel classification by morphological granulometric features. Thesis, Rochester Institute of Technology (1991)Google Scholar
  8. 8.
    Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer (2002)Google Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR, San Diego, USA, pp. 886–893 (2005)Google Scholar
  10. 10.
    Yao, B., Bradski, G., Fei-Fei, L.: A codebook-free and annotation-free approach for fine-grained image categorization. In: CVPR, Providence, USA, pp. 3466–3473 (2012)Google Scholar
  11. 11.
    Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 Dataset. California Institute of Technology (2011) CNS-TR-2011-001Google Scholar
  12. 12.
    Branson, S., Wah, C., Schroff, F., Babenko, B., Welinder, P., Perona, P., Belongie, S.: Visual recognition with humans in the loop. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 438–451. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Lara, A., Hirata Jr., R.: Combining features to a class-specific model in an instance detection framework. SIBGRAPI (2011)Google Scholar
  14. 14.
    Canny, J.: A computational approach to edge detection. IEEE Trans. on PAMI 8, 679–698 (1986)CrossRefGoogle Scholar
  15. 15.
    Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. on PAMI 28, 1465–1479 (2006)CrossRefGoogle Scholar
  16. 16.
    Bosch, A., Zisserman, A., Munoz, X.: Image Classification using Random Forests and Ferns. In: ICCV, Rio de Janeiro, Brazil, pp. 1–8 (2007)Google Scholar
  17. 17.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence, pp. 1137–1145 (1995)Google Scholar
  18. 18.
    Fei-Fei, L., Fergus, R., Perona, P.: One-Shot Learning of Object Categories. IEEE Trans. on PAMI 28, 594–611 (2006)CrossRefGoogle Scholar
  19. 19.
    Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. In: ICCV, Nice, France, pp. 1470–1478 (2003)Google Scholar
  20. 20.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison-Wesley Longman Publishing Co. Inc., Boston (2011)Google Scholar
  21. 21.
    Chih-Fong, T.: Bag-of-Words Representation in Image Annotation: A Review. ISRN Artificial Intelligence 2010 (2012)Google Scholar
  22. 22.
    Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: 12th IAPR Int. Conf. on Pattern Recognition, pp. 582–585 (1994)Google Scholar
  23. 23.
    Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 694–699 (2002)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arnaldo Câmara Lara
    • 1
  • Roberto HirataJr.
    • 1
  1. 1.Institute of Mathematics and StatisticsUniversity of Sao PauloSao PauloBrazil

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