A saliency-based bottom-up visual attention model for dynamicscenes analysis

This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work addsmotion saliency calculations to a neural network model withrealistic temporal dynamics [(e.g., building motion salienceon top of De Brecht and Saiki Neural Networks 19:1467–1474, (2006)]. The resulting...

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Autores:
Ramírez Moreno, David Fernando
Schwartz, Odelia
Ramirez Villegas, Juan Felipe
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/11584
Acceso en línea:
http://red.uao.edu.co//handle/10614/11584
Palabra clave:
Redes neuronales (Computadores)
Neural networks (Computer science)
Visual attention
Saliency map
Motion saliency
Neural network
Synaptic depression
Neural latency
Asymmetry phenomenon
Lyapunov stabilit
Rights
openAccess
License
Derechos Reservados - Universidad Autónoma de Occidente
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oai_identifier_str oai:red.uao.edu.co:10614/11584
network_acronym_str REPOUAO2
network_name_str RED: Repositorio Educativo Digital UAO
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dc.title.eng.fl_str_mv A saliency-based bottom-up visual attention model for dynamicscenes analysis
title A saliency-based bottom-up visual attention model for dynamicscenes analysis
spellingShingle A saliency-based bottom-up visual attention model for dynamicscenes analysis
Redes neuronales (Computadores)
Neural networks (Computer science)
Visual attention
Saliency map
Motion saliency
Neural network
Synaptic depression
Neural latency
Asymmetry phenomenon
Lyapunov stabilit
title_short A saliency-based bottom-up visual attention model for dynamicscenes analysis
title_full A saliency-based bottom-up visual attention model for dynamicscenes analysis
title_fullStr A saliency-based bottom-up visual attention model for dynamicscenes analysis
title_full_unstemmed A saliency-based bottom-up visual attention model for dynamicscenes analysis
title_sort A saliency-based bottom-up visual attention model for dynamicscenes analysis
dc.creator.fl_str_mv Ramírez Moreno, David Fernando
Schwartz, Odelia
Ramirez Villegas, Juan Felipe
dc.contributor.author.none.fl_str_mv Ramírez Moreno, David Fernando
Schwartz, Odelia
Ramirez Villegas, Juan Felipe
dc.subject.armarc.spa.fl_str_mv Redes neuronales (Computadores)
topic Redes neuronales (Computadores)
Neural networks (Computer science)
Visual attention
Saliency map
Motion saliency
Neural network
Synaptic depression
Neural latency
Asymmetry phenomenon
Lyapunov stabilit
dc.subject.armarc.eng.fl_str_mv Neural networks (Computer science)
dc.subject.proposal.eng.fl_str_mv Visual attention
Saliency map
Motion saliency
Neural network
Synaptic depression
Neural latency
Asymmetry phenomenon
Lyapunov stabilit
description This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work addsmotion saliency calculations to a neural network model withrealistic temporal dynamics [(e.g., building motion salienceon top of De Brecht and Saiki Neural Networks 19:1467–1474, (2006)]. The resulting network elicits strong transientresponses to moving objects and reaches stability withina biologically plausible time interval. The responses arestatistically different comparing between earlier and latermotion neural activity; and between moving and non-movingobjects. We demonstrate the network on a number of syn-thetic and real dynamical movie examples. We show thatthe model captures the motion saliency asymmetry phenom-enon. In addition, the motion salience computation enablessudden-onset moving objects that are less salient in the staticscene to rise above others. Finally, we include strong consid-eration for the neural latencies, the Lyapunov stability, andthe neural properties being reproduced by the model
publishDate 2013
dc.date.issued.none.fl_str_mv 2013
dc.date.accessioned.none.fl_str_mv 2019-11-27T15:08:19Z
dc.date.available.none.fl_str_mv 2019-11-27T15:08:19Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.eng.fl_str_mv Ramirez Moreno, D. F; Schwartz, O; Ramirez Villegas, J. F. A saliency-based bottom-up visual attention model for dynamicscenes analysis. Biological Cybernetics. 107(2) (enero 2013). DOI: 10.1007/s00422-012-0542-2
dc.identifier.uri.none.fl_str_mv http://red.uao.edu.co//handle/10614/11584
dc.identifier.doi.spa.fl_str_mv 10.1007/s00422-012-0542-2
identifier_str_mv Ramirez Moreno, D. F; Schwartz, O; Ramirez Villegas, J. F. A saliency-based bottom-up visual attention model for dynamicscenes analysis. Biological Cybernetics. 107(2) (enero 2013). DOI: 10.1007/s00422-012-0542-2
10.1007/s00422-012-0542-2
url http://red.uao.edu.co//handle/10614/11584
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.eng.fl_str_mv Biological Cybernetics. Volumen 107, número 2 (enero 2013)
dc.relation.citationendpage.none.fl_str_mv 160
dc.relation.citationstartpage.none.fl_str_mv 141
dc.relation.ispartofjournal.eng.fl_str_mv Biological cybernetics
dc.relation.references.none.fl_str_mv Abbott LF, Varela JA, Sen K, Nelson SB (1997) Synaptic depression and cortical gain control. Science 275:220–224
Bergen JR, Julesz B (1983) Parallel versus serial processing in rapid pattern discrimination. Nature 303:696–698
Bollman M, Hoischen R, Mertsching B (1997) In: Berlin et al. (ed) Integration of static and dynamic scene features guiding visual attention. Springer, Berlin, pp 483–490
Borst A (2000) Models of motion detection. Nature neuroscience 3:1168 Burt PJ (1988) Proceedings of the 9th international conference on attention mechanisms for vision in dynamic world. Patt Recog 1:977–987
Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31:532–540
Cauller L (1995) Layer I of primary sensory neocortex:where top–down converges upon bottom-up. Behav Brain Res 71:163–170
Chen B (2005) Mathematical models of motion detection in the fly’s visual cortex. Dissertation, Texas Tech University, Texas
Chen LQ, Xie X, Fan X, MaWY, Zhang HJ, Zhou HQ (2003) A visual attention model for adapting images on small displays. Multimed Syst 9:1–12
Coen-Cagli R, Dayan P, Schwartz O (2012) Cortical surround interactions and perceptual salience via natural scene statistics. PLoS Comput Biol 8(3):e1002405
Connor CE, Egeth HE, Yantis S (2004) Visual attention: bottom-up versus top–down. Curr Biol 14:R850–R852
Conway BR (2001) Spatial structure of cone inputs to color cells in alert macaque primary visual cortex (V-1). J Neurosci 21:2768–2783
Conway BR (2009) Color vision, cones and color-coding in the cortex. The Neuroscientist 15:274–290
De Brecht M, Saiki J (2006) A neural network implementation of a saliency map model. Neural Networks 19:1467–1474
Deco G, Rolls ET (2004) A neurodyamical cortical model of visual attention and invariant object recognition. Vis Res 44:621–642
Desimone R, Duncan J (1995) Neural mechanisms of selective visual attention. Ann Rev Neurosci 18:193–222
Desimone R, Ungerleider LG (1989) Neural mechanisms of visual processing in monkeys. Elsevier, New York, pp 267–299
EC Funded CAVIAR project/IST 2001 37540 http://homepages.inf.ed.ac.uk/rbf/CAVIAR/. Accessed Jan 2011
Engel S, Zhang X, Wandell B (1997) Colour tuning in human visual cortex measured with functional magnetic resonance imaging. Nature 388:68–71
Fahy FL, Riches IP, Brown MW (1993) Neuronal activity related to visual recognition memory: long-term memory and the encoding of recency and familiarity information in the primate anterior and medial inferior and rhinal cortex. Exp Brain Res 96:457–472
Fix J, Rougier N, Alexandre F (2010) A dynamic neural field approach to the covert and overt deployment of spatial attention. Cogn Comput 3:279–293
Gao D, Vasconcelos N (2007) Bottom-up saliency is a discriminant process. Proceedings of the IEEE international conference on computer vision, Rio de Janeiro
Gonzalez Andino SL, de Peralta Grave (2012) Coding of saliency by ensemble bursting in the amygdala of primates. Front Behav Neurosci 6(38):1–16
Greenspan H, Belongie S, Goodman R, Perona P, Rakshit S, Anderson CH (1994) Overcomplete steerable pyramid filters and rotation invariance. Proc IEEE Comput Vis Patt Recog 1:222–228
Hamker FH (2004) A dynamic model of how feature cues guide spatial attention. Vis Res 44:501–521
Hamker FH (2006) Modeling feature-based attention as an active top– down inference process. BioSystems 86:91–99
Horowitz TS, Wolfe JM, DiMase JS, Klieger SB (2007) Visual search for type of motion is based on simple motion primitives. Perception 36:1624–1634
Ibbotson M (2001) Identification of mechanisms underlying motion detection in mammals. Springer, Berlin
Itti L (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Proc 13:1304–1318
Itti L, Koch C (2000) A saliency-based search mechanism for overt and covert shifts of visual attention. Vis Res 40:1489–1506
Itti L, Koch C (2001) Computational modeling of visual attention. Nat Rev Neurosci 2:194–203
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Patt Anal Mach Intel 20:1254–1259
Jonides J,Yantis S (1988) Uniqueness of abrupt visual onset in capturing attention. Percept Psychophys 43:346–354
Kusunoki M, Gottlieb J, Goldberg ME (2000) The lateral intraparietal area as a salience map: the representation of abrupt onset, stimulus motion, and task relevance. Vis Res 40:1459–1468
Li Z (1999) Contextual influences in V1 as a basis for pop out and asymmetry in visual search. ProcNatlAcad Sci 96(18):10530–10535
Li Z (2002) A saliency map in primary visual cortex. Trend Cogn Sci 6:9–16
Liu T, Sun J, Zheng NN, Tang X, Shum HY (2007) Learning to detect a salient object. Proceedings of IEEE computer society conference on computer and vision pattern recognition, Providence
López MT, Fenández-Caballero A, Fernández MA, Mira J, Delgado AE (2006) Motion features to enhance scene segmentation in active visual attention. Patt Recog Lett 27:469–478
Mahadevan V, Vasconcelos N (2010) Spatiotemporal saliency in dynamic scenes. IEEE Trans Pattern Anal Mach Intell 32: 171–177
Matsuno T, Tomonaga M (2006) Visual search for moving and stationary items in chimpanzees (Pan troglodytes) and humans (Homo sapiens). Behav Brain Res 172:219–232
Maunsell JHR, Treue S (2006) Feature-based attention in visual cortex. Trends Neurosci 29:317–322
Meso AI, Zanker JM (2009) Speed encoding in correlation motion detectors as a consequence of spatial structure. Biological Cybern 100:361–370
Mira J, Delgado AE, Lopez MT, Fernandez-Caballero A, FernandezMA (2006) A conceptual frame with two neural mechanisms to model selective visual attention processes. Neurocomputing 71:704–720
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spelling Ramírez Moreno, David Fernandovirtual::4308-1Schwartz, Odeliacfbc0dfdb128f8537c95b0409dcd92e6Ramirez Villegas, Juan Felipede4a5d2f855a047341c6903be500d787Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-11-27T15:08:19Z2019-11-27T15:08:19Z2013Ramirez Moreno, D. F; Schwartz, O; Ramirez Villegas, J. F. A saliency-based bottom-up visual attention model for dynamicscenes analysis. Biological Cybernetics. 107(2) (enero 2013). DOI: 10.1007/s00422-012-0542-2http://red.uao.edu.co//handle/10614/1158410.1007/s00422-012-0542-2This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work addsmotion saliency calculations to a neural network model withrealistic temporal dynamics [(e.g., building motion salienceon top of De Brecht and Saiki Neural Networks 19:1467–1474, (2006)]. The resulting network elicits strong transientresponses to moving objects and reaches stability withina biologically plausible time interval. The responses arestatistically different comparing between earlier and latermotion neural activity; and between moving and non-movingobjects. We demonstrate the network on a number of syn-thetic and real dynamical movie examples. We show thatthe model captures the motion saliency asymmetry phenom-enon. In addition, the motion salience computation enablessudden-onset moving objects that are less salient in the staticscene to rise above others. Finally, we include strong consid-eration for the neural latencies, the Lyapunov stability, andthe neural properties being reproduced by the modelapplication/pdfengSpringerBiological Cybernetics. Volumen 107, número 2 (enero 2013)160141Biological cyberneticsAbbott LF, Varela JA, Sen K, Nelson SB (1997) Synaptic depression and cortical gain control. Science 275:220–224Bergen JR, Julesz B (1983) Parallel versus serial processing in rapid pattern discrimination. Nature 303:696–698Bollman M, Hoischen R, Mertsching B (1997) In: Berlin et al. (ed) Integration of static and dynamic scene features guiding visual attention. Springer, Berlin, pp 483–490Borst A (2000) Models of motion detection. Nature neuroscience 3:1168 Burt PJ (1988) Proceedings of the 9th international conference on attention mechanisms for vision in dynamic world. Patt Recog 1:977–987Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31:532–540Cauller L (1995) Layer I of primary sensory neocortex:where top–down converges upon bottom-up. Behav Brain Res 71:163–170Chen B (2005) Mathematical models of motion detection in the fly’s visual cortex. Dissertation, Texas Tech University, TexasChen LQ, Xie X, Fan X, MaWY, Zhang HJ, Zhou HQ (2003) A visual attention model for adapting images on small displays. Multimed Syst 9:1–12Coen-Cagli R, Dayan P, Schwartz O (2012) Cortical surround interactions and perceptual salience via natural scene statistics. PLoS Comput Biol 8(3):e1002405Connor CE, Egeth HE, Yantis S (2004) Visual attention: bottom-up versus top–down. Curr Biol 14:R850–R852Conway BR (2001) Spatial structure of cone inputs to color cells in alert macaque primary visual cortex (V-1). J Neurosci 21:2768–2783Conway BR (2009) Color vision, cones and color-coding in the cortex. The Neuroscientist 15:274–290De Brecht M, Saiki J (2006) A neural network implementation of a saliency map model. Neural Networks 19:1467–1474Deco G, Rolls ET (2004) A neurodyamical cortical model of visual attention and invariant object recognition. Vis Res 44:621–642Desimone R, Duncan J (1995) Neural mechanisms of selective visual attention. Ann Rev Neurosci 18:193–222Desimone R, Ungerleider LG (1989) Neural mechanisms of visual processing in monkeys. Elsevier, New York, pp 267–299EC Funded CAVIAR project/IST 2001 37540 http://homepages.inf.ed.ac.uk/rbf/CAVIAR/. Accessed Jan 2011Engel S, Zhang X, Wandell B (1997) Colour tuning in human visual cortex measured with functional magnetic resonance imaging. Nature 388:68–71Fahy FL, Riches IP, Brown MW (1993) Neuronal activity related to visual recognition memory: long-term memory and the encoding of recency and familiarity information in the primate anterior and medial inferior and rhinal cortex. Exp Brain Res 96:457–472Fix J, Rougier N, Alexandre F (2010) A dynamic neural field approach to the covert and overt deployment of spatial attention. Cogn Comput 3:279–293Gao D, Vasconcelos N (2007) Bottom-up saliency is a discriminant process. Proceedings of the IEEE international conference on computer vision, Rio de JaneiroGonzalez Andino SL, de Peralta Grave (2012) Coding of saliency by ensemble bursting in the amygdala of primates. Front Behav Neurosci 6(38):1–16Greenspan H, Belongie S, Goodman R, Perona P, Rakshit S, Anderson CH (1994) Overcomplete steerable pyramid filters and rotation invariance. Proc IEEE Comput Vis Patt Recog 1:222–228Hamker FH (2004) A dynamic model of how feature cues guide spatial attention. Vis Res 44:501–521Hamker FH (2006) Modeling feature-based attention as an active top– down inference process. BioSystems 86:91–99Horowitz TS, Wolfe JM, DiMase JS, Klieger SB (2007) Visual search for type of motion is based on simple motion primitives. Perception 36:1624–1634Ibbotson M (2001) Identification of mechanisms underlying motion detection in mammals. Springer, BerlinItti L (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Proc 13:1304–1318Itti L, Koch C (2000) A saliency-based search mechanism for overt and covert shifts of visual attention. Vis Res 40:1489–1506Itti L, Koch C (2001) Computational modeling of visual attention. Nat Rev Neurosci 2:194–203Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Patt Anal Mach Intel 20:1254–1259Jonides J,Yantis S (1988) Uniqueness of abrupt visual onset in capturing attention. Percept Psychophys 43:346–354Kusunoki M, Gottlieb J, Goldberg ME (2000) The lateral intraparietal area as a salience map: the representation of abrupt onset, stimulus motion, and task relevance. Vis Res 40:1459–1468Li Z (1999) Contextual influences in V1 as a basis for pop out and asymmetry in visual search. ProcNatlAcad Sci 96(18):10530–10535Li Z (2002) A saliency map in primary visual cortex. Trend Cogn Sci 6:9–16Liu T, Sun J, Zheng NN, Tang X, Shum HY (2007) Learning to detect a salient object. Proceedings of IEEE computer society conference on computer and vision pattern recognition, ProvidenceLópez MT, Fenández-Caballero A, Fernández MA, Mira J, Delgado AE (2006) Motion features to enhance scene segmentation in active visual attention. Patt Recog Lett 27:469–478Mahadevan V, Vasconcelos N (2010) Spatiotemporal saliency in dynamic scenes. IEEE Trans Pattern Anal Mach Intell 32: 171–177Matsuno T, Tomonaga M (2006) Visual search for moving and stationary items in chimpanzees (Pan troglodytes) and humans (Homo sapiens). Behav Brain Res 172:219–232Maunsell JHR, Treue S (2006) Feature-based attention in visual cortex. Trends Neurosci 29:317–322Meso AI, Zanker JM (2009) Speed encoding in correlation motion detectors as a consequence of spatial structure. Biological Cybern 100:361–370Mira J, Delgado AE, Lopez MT, Fernandez-Caballero A, FernandezMA (2006) A conceptual frame with two neural mechanisms to model selective visual attention processes. Neurocomputing 71:704–720Mundhenk TN, Itti L (2005) Computational modeling and exploration of contour integration for visual saliency. Biol Cybern 93:188–212Nagy AL, Cone SM (1996) Asymmetries in simple feature searches for color. Vis Res 36:2837–2847 NavalpakkamV, Itti L (2002)Agoal oriented attention guidancemodel. Lect Notes Comput Sci 2525:453–461Navalpakkam V, Itti L (2005) Modeling the influence of task on attention. Vision Res. 45:205–231Navalpakkam V, Itti L (2006) Modeling the influence of task on attention. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 1:1–8Nothdurft H (2000) Salience from feature contrast: additivity across dimensions. Vis Res 40:1183–1201Oliva A, Torralba A, CastelhanoMS, Henderson JM (2003) Top–down control of visual attention in object detection. Proc Int Conf Image Proc 1:253–256Park SJ, An KH, Lee M (2002) Saliency map model with adaptive masking based on independent component analysis. Neurocomputing 49:417–422Peters RJ, Iyer A, Itti L, Koch C (2005) Components of bottom-up gaze allocation in natural images. Vis Res 45:2397–2416Pointing Gestures: Video Sequence Database (ICPR Workshop, Cambridge, United Kingdom). http://www.cvmt.dk/. Accessed Jan 2011Pomplun M (2007) Advancing area activation towards a general model of eye movements in visual search. 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