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Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models

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Attention in Cognitive Systems (WAPCV 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5395))

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Abstract

Every field of science requires standardization of metrics and measurement methods for detecting true advancement in research. Efforts on computational models of visual attention models have increased in the recent years and now it is important to have standard measuring techniques in this area in order to avoid undue deceleration in its progress. This paper performs a review of the evaluation techniques used by different researchers in the field and brings them in an organized structure. Further methods and metrics are also proposed that would lead to more objective and quantitative evaluation of the attention models.

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References

  1. Itti, L., Koch, U., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. Transactions on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)

    Article  Google Scholar 

  2. Peters, R.J., Iyer, A., Itti, L., Koch, C.: Components of bottom-up gaze allocation in natural images. Vision Research 45, 2397–2416 (2005)

    Article  PubMed  Google Scholar 

  3. Avraham, T., Lindenbaum, M.: Esaliency - a stochastic attention model incorporating similarity information and knowledge-based preferences. In: WRUPKV-ECCV 2006, Graz, ECCV 2006 (2006)

    Google Scholar 

  4. Baltazar, J., Pinho, P., Pereira, F.: Visual attention driven image to video transmoding. In: Picture Coding Symposium (PCS 2006), Beijing, China (2006)

    Google Scholar 

  5. Chen, L., Xie, X., Fan, X., Ma, W., Zhang, H., Zhou, H.: A visual attention model for adapting images on small displays. ACM Multimedia Systems Journal 9, 353–364 (2003)

    Article  Google Scholar 

  6. Privitera, C.M., Stark, L.W.: Algorithms for defining visual regions-of-interest: Comparison with eye fixations. Transactions on Pattern Analysis and Machine Intelligence 9, 970–982 (2000)

    Article  Google Scholar 

  7. Clauss, M., Bayerl, P., Neumann, H.: A statistical measure for evaluating regions-of-interest based attention algorithms. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 383–390. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Draper, B.A., Lionelle, A.: Evaluation of selective attention under similarity transforms. In: WAPCV 2003 (2003)

    Google Scholar 

  9. Marmitt, G., Duchowski, A.T.: Modeling visual attention in vr:measuring the accuracy of predicted scanpaths. In: EUROGRAPHICS 2002 (2002)

    Google Scholar 

  10. Meur, O.L., Callet, P.L., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. Transactions on Pattern Analysis and Machine Intelligence 28, 802–817 (2006)

    Article  Google Scholar 

  11. Hügli, H., Jost, T., Ouerhani, N.: Model performance for visual attention in real 3D color scenes. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 469–478. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  12. Aziz, M.Z., Mertsching, B.: Fast and robust generation of feature maps for region-based visual attention. Transactions on Image Processing 17, 633–644 (2008)

    Article  PubMed  Google Scholar 

  13. Michalke, T., Gepperth, A., Schneider, M., Fritsch, J., Goerick, C.: Towards a human-like vision system for resource-constrained intelligent cars. In: ICVS 2007, Bielefeld University eCollections, Germany, pp. 264–275 (2004)

    Google Scholar 

  14. Hawes, N., Wyatt, J.: Towards context-sensitive visual attention. In: Second International Cognitive Vision Workshop (ICVW 2006) (2006)

    Google Scholar 

  15. Frintrop, S., Backer, G., Rome, E.: Goal-directed search with a top-down modulated computational attention system. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 117–124. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Navalpakkam, V., Itti, L.: Modeling the influence of task on attention. Vision Research, 205–231 (2005)

    Google Scholar 

  17. Aziz, M.Z., Mertsching, B.: An attentional approach for perceptual grouping of spatially distributed patterns. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 345–354. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Aziz, M.Z., Mertsching, B.: Pop-out and IOR in static scenes with region based visual attention. In: WCAA-ICVS 2007, Bielefeld - Germany, Bielefeld University eCollections (2007)

    Google Scholar 

  19. Aziz, M.Z., Mertsching, B.: Color saliency and inhibition using static and dynamic scenes in region based visual attention. In: Paletta, L., Rome, E. (eds.) WAPCV 2007. LNCS (LNAI), vol. 4840, pp. 234–250. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Aziz, M.Z., Mertsching, B. (2009). Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models. In: Paletta, L., Tsotsos, J.K. (eds) Attention in Cognitive Systems. WAPCV 2008. Lecture Notes in Computer Science(), vol 5395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00582-4_17

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  • DOI: https://doi.org/10.1007/978-3-642-00582-4_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00581-7

  • Online ISBN: 978-3-642-00582-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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