Associative Memories Applied to Image Categorization

  • Roberto A. Vázquez
  • Humberto Sossa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

In this paper we describe how associative memories can be applied to categorize images. If we present to an associative memory (AM) an image we would expect that the AM would respond with something that describes the content of the image; for example, if the image contains a tiger we would expect that the AM would respond with the word “tiger”. In order to achieve this goal, we first chose a set of images. Each image is next associated to the word that better describes the content of the image. With this information an AM is trained as in [10]. We then use the AM to categorize instances of images with the same content even if these images are distorted by some kind of noise. The accuracy of the proposal is tested using a set of images containing different species of flowers and animals.

Download to read the full conference paper text

References

  1. 1.
    Steinbuch, K.: Die Lernmatrix. Kybernetik 1(1), 26–45 (1961)CrossRefGoogle Scholar
  2. 2.
    Anderson, J.A.: A simple neural network generating an interactive memory. Mathematical Biosciences 14, 197–220 (1972)MATHCrossRefGoogle Scholar
  3. 3.
    Kohonen, T.: Correlation matrix memories. IEEE Trans. on Computers 21(4), 353–359 (1972)MATHCrossRefGoogle Scholar
  4. 4.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 79, 2554–2558 (1982)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Sussner, P.: Generalizing operations of binary auto-associative morphological memories using fuzzy set theory. Journal of mathematical Imaging and Vision 19(2), 81–93 (2003)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Ritter, G.X., Urcid, G., Iancu, L.: Reconstruction of patterns from noisy inputs using morphological associative memories. Journal of mathematical Imaging and Vision 19(2), 95–111 (2003)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Sossa, H., Barron, R.: New associative model for pattern recall in the presence of mixed noise. In: Proceedings of the fithh IASTED International Conference on Signal and Image Processing, SIP 2003, vol. 399, pp. 485–490. Acta Press (2003)Google Scholar
  8. 8.
    Sossa, H., Barrón, R., Vázquez, R.A.: Transforming Fundamental set of Patterns to a Canonical Form to Improve Pattern Recall. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 687–696. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Sossa, H., Barrón, R., Vázquez, R.A.: New associative memories for recall real-valued patterns. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004. LNCS, vol. 3287, pp. 195–202. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Vázquez, R.A., Sossa, H., Barrón, R.: Enhanced associative memory model for pattern restoration (submitted, 2006)Google Scholar
  11. 11.
    Vázquez, R.A., Sossa, H.: Associative memories applied to image retrieval. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, Springer, Heidelberg (2006)Google Scholar
  12. 12.
    Mojsilović, A., Gomes, J., Rogowitz, B.: Semantic-Friendly Indexing and Quering of Images Based on the Extraction of the Objective Semantic Cues. International Journal of Computer Vision, Special Issue on Content-Based Image Retrieval 56(1-2), 79–107 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Roberto A. Vázquez
    • 1
  • Humberto Sossa
    • 1
  1. 1.Centro de Investigación en Computación – IPNCiudad de MéxicoMéxico

Personalised recommendations