Abstract
Brain informatics (BI) is a field of interdisciplinary study covering topics such as attention, memory, language, computation, learning and creativity, just to say a few. The BI is responsible for studying the mechanisms of human information processing. The dorsal stream, or “where”stream, is intimately related to the processing of visual attention. This paper proposes to evolve VAPs that learn to attend a given object within a scene. Visual attention is usually divided in two stages: feature acquisition and feature integration. In both phases there are specialized operators in the acquisition of a specific feature, called EVOs, and on the fusion of these features, called EFI. In previous research, those referred operators were established without considering the goal to be achieved. Instead of using established operators the idea is to learn and optimize them for the visual attention task. During the experiments we used a standard database of images for visual attention. The results provided in this paper show that our approach achieves impressive performance in the problem of focus visual attention over complex objects in challenging real world images on the first try.
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References
Atmosukarto, I., Shapiro, L.G., Heike, C.: The use of genetic programming for learning 3D craniofacial shape quantifications. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR 2010 pp. 2444–2447 (2010)
Barton, R.A.: Visual Specialization and Brain Evolution in Primates. Proceedings of the Royal Society of London Saries B-Biological Sciences 265(1409), 1933–1937 (1998)
Corbetta, M., Shulman, G.L.: Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience 3, 201–215 (2002)
Desimone, R., Duncan, J.: Neural Mechanisms of Selective Visual Attention. Annual Reviews 18, 193–222 (1995)
Feldman, J.A.: Four frames suffice: A provisional model of vision and space. Behavioral and Brain Sciences 8, 265–289 (1985)
Gottlieb, J.: From thought to action: the parietal cortex as a bridge between perception, action, and cognition. Neuron 53, 9–16 (2007)
Hubel, D., Wiesel, T.: Receptive Fields of Single Neurones in the Cat Striate Cortex. J. Physiol. 148, 574–591 (1959)
Itti, L., Koch, C.: Feature Combination Strategies for Saliency-Based Visual Attention Systems. Journal of Electronic Imaging 10(1), 161–169 (2001)
Kosh, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4, 219–227 (1985)
Perez, C.B., Olague, G.: Learning Invariant Region Descriptor Operators with Genetic Programming and the F-Measure. In: 19th International Conference on Pattern Recognition, ICPR 2008 (2008)
Rensink, R.A.: Seeing, sensing, and scrutinizing. Vision Research 40, 1469–1487 (2000)
Treisman, A.M., Gelade, G.: A Feature-Integration Theory of Attention. Cognitive Psichology 12(1), 97–136 (1980)
Walther, D., Kosh, C.: Modeling Attention to Salient Proto-Objects. Neural Networks 19, 1395–1407 (2006)
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Dozal, L., Olague, G., Clemente, E., Sánchez, M. (2012). Evolving Visual Attention Programs through EVO Features. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_33
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DOI: https://doi.org/10.1007/978-3-642-29178-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29177-7
Online ISBN: 978-3-642-29178-4
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