Uneingeschränkter Zugang

Applying LCS To Affective Image Classification In Spatial-Frequency Domain


Zitieren

[1] Kuo, W.J., et al., Intuition and Deliberation: Two Systems for Strategizing in the Brain. Science, 2009. 324(5926): p. 519-522.10.1126/science.1165598Search in Google Scholar

[2] Chowdhury, R.M.M.I., G.D. Olsen, and J.W. Pracejus, Affective Responses to Images In Print Advertising: Affect Integration in a Simultaneous Presentation Context. Journal of Advertising, 2008. 37(3): p. 7-18.10.2753/JOA0091-3367370301Search in Google Scholar

[3] Chang, C., The Impacts of Emotion Elicited By Print Political Advertising on Candidate Evaluation. Media Psychology, 2001. 3(2): p. 91-118.10.1207/S1532785XMEP0302_01Search in Google Scholar

[4] Kyung-Sun, K., Effects of emotion control and task on Web searching behavior. Information Processing & Management, 2008. 44(1): p. 373-385.10.1016/j.ipm.2006.11.008Search in Google Scholar

[5] Mitchell, T.M., Machine learning. 1997. Burr Ridge, IL: McGraw Hill, 1997. 45.Search in Google Scholar

[6] Orriols-Puig, A., J. Casillas, and E. Bernad-Mansilla, Genetic-based machine learning systems are competitive for pattern recognition. Evolutionary Intelligence, 2008. 1(3): p. 209-232.10.1007/s12065-008-0013-9Search in Google Scholar

[7] Russell, S. and P. Norvig, Artificial Intelligence: A Modern Approach (2nd Edition)2002: Prentice Hall.Search in Google Scholar

[8] Wilson, S.W., Classifier fitness based on accuracy. Evol. Comput., 1995. 3(2): p. 149-175.Search in Google Scholar

[9] Picard, R.W., Affective Computing2000, Cambridge MA: The MIT Press.Search in Google Scholar

[10] Ortony, A. and T. Turner, What’s basic about basic emotions. Psychological review, 1990.10.1037/0033-295X.97.3.315Search in Google Scholar

[11] Bradley, M.M., Emotional memory: a dimensional analysis, in Emotions: Essays on emotion theory, S.H.M.v. Goozen, N.E.v.d. Poll, and J.A. Sergeant, Editors. 1994, Lawrence Erlbaum: Hillsdale, NJ. p. 97-134.Search in Google Scholar

[12] Bradley, M.M. and P.J. Lang, Emotion and motivation, in Handbook of Psychophysiology, J.T. Cacioppo, L.G. Tassinary, and G. Berntson, Editors. 2007, Cambridge University Press: New York, NY. p. 581-607.Search in Google Scholar

[13] Lang, P.J., The motivational organization of emotion: Affect-reflex connections, in Emotions: Essays on emotion theory1994, Lawrence Erlbaum: Hillsdale, NJ. p. 61-93.Search in Google Scholar

[14] Lang, P.J., The Emotion Probe - Studies of Motivation and Attention. American Psychologist, 1995. 50(5): p. 372-385.10.1037/0003-066X.50.5.372Search in Google Scholar

[15] Bolls, P.D., A. Lang, and R.F. Potter, The Effects of Message Valence and Listener Arousal on Attention, Memory, and Facial Muscular Responses to Radio Advertisements. Communication Research, 2001. 28: p. 627-651.10.1177/009365001028005003Search in Google Scholar

[16] Antonio R, D., Emotion in the perspective of an integrated nervous system. Brain Research Reviews, 1998. 26(2-3): p. 83-86.10.1016/S0165-0173(97)00064-7Search in Google Scholar

[17] Bechara, A., The role of emotion in decisionmaking: Evidence from neurological patients with orbitofrontal damage. Brain and cognition, 2004. 55(1): p. 30-40.10.1016/j.bandc.2003.04.00115134841Search in Google Scholar

[18] LaBar, K.S. and R. Cabeza, Cognitive neuroscience of emotional memory. Nat Rev Neurosci, 2006. 7(1): p. 54-64.10.1038/nrn182516371950Search in Google Scholar

[19] Wu, Q., C. Zhou, and C. Wang, Content-Based Affective Image Classification and Retrieval Using Support Vector Machines, in Affective Computing and Intelligent Interaction, J. Tao, T. Tan, and R. Picard, Editors. 2005, Springer Berlin / Heidelberg. p. 239-247.10.1007/11573548_31Search in Google Scholar

[20] Joshi, D., et al., Aesthetics and Emotions in Images. Signal Processing Magazine, IEEE, 2011. 28(5): p. 94-115.10.1109/MSP.2011.941851Search in Google Scholar

[21] Liu, N., et al., Associating Textual Features with Visual Ones to Improve Affective Image Classification, in Affective Computing and Intelligent Interaction, S. D’Mello, et al., Editors. 2011, Springer Berlin / Heidelberg. p. 195-204.10.1007/978-3-642-24600-5_23Search in Google Scholar

[22] Machajdik, J. and A. Hanbury, Affective image classification using features inspired by psychology and art theory, in Proceedings of the international conference on Multimedia2010, ACM: Firenze, Italy. p. 83-92.10.1145/1873951.1873965Search in Google Scholar

[23] Zhang, H., et al., Analyzing Emotional Semantics of Abstract Art Using Low-Level Image Features, in Advances in Intelligent Data Analysis X, J. Gama, E. Bradley, and J. Hollmn, Editors. 2011, Springer Berlin / Heidelberg. p. 413-423.10.1007/978-3-642-24800-9_38Search in Google Scholar

[24] Lang, P.J., M.M. Bradley, and B.N. Cuthbert, International affective picture system (IAPS): Affective ratings of pictures and instruction manual, 2008: University of Florida, Gainesville, FL.Search in Google Scholar

[25] Sanchez-Navarro, J., et al., Psychophysiological, behavioral, and cognitive indices of the emotional response: A factor-analytic study. Spanish Journal of Psychology, 2008. 11(1): p. 16-25.10.1017/S113874160000407818630644Search in Google Scholar

[26] Kensinger, E.A., R.J. Garoff-Eaton, and D.L. Schacter, Effects of emotion on memory specificity: Memory trade-offs elicited by negative visually arousing stimuli. Journal of Memory and Language, 2007. 56(4): p. 575-591.10.1016/j.jml.2006.05.004Search in Google Scholar

[27] Lang, P.J., Behavioral treatment and biobehavioral assessment: Computer applications, in Technology in Mental Health Care Delivery Systems, J. Sidowski, J. Johnson, and T. Williams, Editors. 1980, Ablex Pub. Corp.: Norwood, NJ. p. 119-137.Search in Google Scholar

[28] Morris, J.D., Observations: SAM: the Self-Assessment Manikin; an efficient cross-cultural measurement of emotional response. Journal of advertising research, 1995. 35(6): p. 63-68.Search in Google Scholar

[29] Mehrabian, A. and J.A. Russell, An approach to environmental psychology1974, Cambridge, MA: the MIT Press.Search in Google Scholar

[30] Bradley, M.M. and P.J. Lang, The International Affective Digitized Sounds (2nd Edition; IADS-2): Affective ratings of sounds and instruction manual. University of Florida, Gainesville, FL, Tech. Rep. B-3, 2007.Search in Google Scholar

[31] Holland, J.H., Adaptation in Natural and Artificial System1992, Cambridge, MA, USA: MIT Press.Search in Google Scholar

[32] Wilson, S.W., ZCS: A Zeroth Level Classifier System. Evolutionary Computation, 1994. 2(1): p. 1-18.10.1162/evco.1994.2.1.1Search in Google Scholar

[33] Wilson, S.W., Get Real! XCS with Continuous-Valued Inputs. Learning Classifier Systems, 2000. 1813: p. 209-219.10.1007/3-540-45027-0_11Search in Google Scholar

[34] Stone, C. and L. Bull, For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation, 2003. 11(3): p. 299-336.10.1162/10636560332236531514558914Search in Google Scholar

[35] Dam, H.H., H.A. Abbass, and C. Lokan, Be real! XCS with continuous-valued inputs, in Proceedings of the 2005 workshops on Genetic and evolutionary computation2005, ACM: Washington, D.C. p. 85-87.10.1145/1102256.1102274Search in Google Scholar

[36] Lanzi, P.L. Adding memory to XCS. in IEEE World Congress on Computational Intelligence. 1998.Search in Google Scholar

[37] Wilson, S.W., Compact Rulesets from XCSI, in Advances in Learning Classifier Systems, P. Lanzi, W. Stolzmann, and S. Wilson, Editors. 2002, Springer Berlin / Heidelberg. p. 65-92.10.1007/3-540-48104-4_12Search in Google Scholar

[38] Dam, H.H., H.A. Abbass, and C. Lokan, DXCS: an XCS system for distributed data mining, in Proceedings of the 2005 conference on Genetic and evolutionary computation2005, ACM: Washington DC, USA. p. 1883-1890.10.1145/1068009.1068326Search in Google Scholar

[39] Wilson, S.W., Classifiers that approximate functions. Natural Computing, 2002. 1(2): p. 211-234.10.1023/A:1016535925043Search in Google Scholar

[40] Lanzi, P.L., et al., Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension. Evol. Comput., 2007. 15(2): p. 133-168.Search in Google Scholar

[41] Bull, L., E. Bernad-Mansilla, and J. Holmes, Learning Classifier Systems in Data Mining: An Introduction, in Learning Classifier Systems in Data Mining, L. Bull, E. Bernad-Mansilla, and J. Holmes, Editors. 2008, Springer Berlin / Heidelberg. p. 1-15.10.1007/978-3-540-78979-6_1Search in Google Scholar

[42] Butz, M., et al., Knowledge Extraction and Problem Structure Identification in XCS, in Parallel Problem Solving from Nature - PPSN VIII, X. Yao, et al., Editors. 2004, Springer Berlin / Heidelberg. p. 1051-1060.10.1007/978-3-540-30217-9_106Search in Google Scholar

[43] Muruzbal, J., A probabilistic classifier system and its application in data mining. Evol. Comput., 2006. 14(2): p. 183-221.Search in Google Scholar

[44] Orriols-Puig, A., J. Casillas, and E. Bernad-Mansilla, First approach toward on-line evolution of association rules with learning classifier systems, in Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation2008, ACM: Atlanta, GA, USA. p. 2031-2038.10.1145/1388969.1389017Search in Google Scholar

[45] Dam, H., C. Lokan, and H. Abbass, Evolutionary Online Data Mining: An Investigation in a Dynamic Environment, in Evolutionary Computation in Dynamic and Uncertain Environments, S. Yang, Y.-S. Ong, and Y. Jin, Editors. 2007, Springer Berlin / Heidelberg. p. 153-178.10.1007/978-3-540-49774-5_7Search in Google Scholar

[46] Quirin, A., et al. Analysis and evaluation of learning classifier systems applied to hyperspectral image classification. in Intelligent Systems Design and Applications, 2005. ISDA ’05. Proceedings. 5th International Conference on. 2005.10.1109/ISDA.2005.23Search in Google Scholar

[47] Butz, M., et al., Effective and Reliable Online Classification Combining XCS with EDA Mechanisms, in Scalable Optimization via Probabilistic Modeling, M. Pelikan, K. Sastry, and E. CantPaz, Editors. 2006, Springer Berlin / Heidelberg. p. 249-273.10.1007/978-3-540-34954-9_11Search in Google Scholar

[48] Akbar, M.A. and M. Farooq, Application of evolutionary algorithms in detection of SIP based flooding attacks, in Proceedings of the 11th Annual conference on Genetic and evolutionary computation2009, ACM: Montreal, Canada. p. 1419-1426.10.1145/1569901.1570092Search in Google Scholar

[49] Armano, G., A. Murru, and F. Roli, Stock Market Prediction by a Mixture of Genetic-Neural Experts. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 2002. 16(5): p. 501-526.10.1142/S0218001402001861Search in Google Scholar

[50] Tsai, W.-C. and A.-P. Chen. Global Asset Allocation Using XCS Experts in Country-Specific ETFs. in Convergence and Hybrid Information Technology, 2008. ICCIT ’08. Third International Conference on. 2008.10.1109/ICCIT.2008.418Search in Google Scholar

[51] Sprogar, M., M. Sprogar, and M. Colnaric, Autonomous evolutionary algorithm in medical data analysis. Computer Methods and Programs in Biomedicine, 2005. 80(Supplement 1): p. S29-S38.10.1016/S0169-2607(05)80004-5Search in Google Scholar

[52] Passaro, A., F. Baronti, and V. Maggini, Exploring relationships between genotype and oral cancer development through XCS, in Proceedings of the 2005 workshops on Genetic and evolutionary computation2005, ACM:Washington, D.C. p. 147-151.10.1145/1102256.1102289Search in Google Scholar

[53] Baronti, F., et al., Machine learning contribution to solve prognostic medical, in Outcome prediction in cancer, A. Taktak and A.C. Fisher, Editors. 2007, Elsevier.10.1016/B978-044452855-1/50012-XSearch in Google Scholar

[54] Bernauer, A., et al., Combining Software and Hardware LCS for Lightweight On-chip Learning, in Organic Computing — A Paradigm Shift for Complex Systems, C. Mller-Schloer, H. Schmeck, and T. Ungerer, Editors. 2011, Springer Basel. p. 253-265.10.1007/978-3-0348-0130-0_16Search in Google Scholar

[55] Bernauer, A., et al., Autonomous multi-processor-SoC optimization with distributed learning classifier systems XCS, in Proceedings of the 8th ACM international conference on Autonomic computing2011, ACM: Karlsruhe, Germany. p. 213-216.10.1145/1998582.1998632Search in Google Scholar

[56] Armano, G., NXCS Experts for Financial Time Series Forecasting, in Applications of Learning Classifier Systems, L. Bull, Editor 2004, Springer. p. 68-91.10.1007/978-3-540-39925-4_3Search in Google Scholar

[57] Chen, A.-P. and Y.-H. Chang. Using extended classifier system to forecast S&P futures based on contrary sentiment indicators. in Evolutionary Computation, 2005. The 2005 IEEE Congress on. 2005.Search in Google Scholar

[58] Chen, A.-P., et al., Applying the Extended Classifier System to Trade Interest Rate Futures Based on Technical Analysis, in Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 032008, IEEE Computer Society. p. 598-603.10.1109/ISDA.2008.219Search in Google Scholar

[59] Shankar, A. and S.J. Louis, XCS for Personalizing Desktop Interfaces. Evolutionary Computation, IEEE Transactions on, 2010. 14(4): p. 547-560.10.1109/TEVC.2009.2021466Search in Google Scholar

[60] Butz, M. and S. Wilson, An Algorithmic Description of XCS, in Advances in Learning Classifier Systems, P. Luca Lanzi,W. Stolzmann, and S.Wilson, Editors. 2001, Springer Berlin / Heidelberg. p. 267-274. Wilson, S.W., Generalization in the XCS Classifier System, 1998.Search in Google Scholar

[61] Mikels, J., et al., Emotional category data on images from the international affective picture system. Behavior Research Methods, 2005. 37(4): p. 626-630.10.3758/BF03192732Search in Google Scholar

[62] Bradley, M.M. and P.J. Lang, Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 1994. 25: p. 49-59.10.1016/0005-7916(94)90063-9Search in Google Scholar

[63] Cohen, I., et al., Facial expression recognition from video sequences: temporal and static modeling. Computer Vision and Image Understanding, 2003. 91(1-2): p. 160-187.10.1016/S1077-3142(03)00081-XSearch in Google Scholar

[64] Kim, K.H., S.W. Bang, and S.R. Kim, Emotion recognition system using short-term monitoring of physiological signals. Medical & Biological Engineering & Computing, 2004. 42(3): p. 419-427.10.1007/BF0234471915191089Search in Google Scholar

[65] Lang, P.J., M.M. Bradley, and B.N. Cuthbert, International Affective Picture System (IAPS), in Techinal Manual and Affective Ratings1999, The Center for Research in Psychophysiology, University of Florida: Gainesville, FL.Search in Google Scholar

[66] Stalph, P.O. and M.V. Butz, Documentation of JavaXCSF, 2009: Retrieved from University of Wurzburg, Cognitive Bodyspaces: Learning and Behavior website.Search in Google Scholar

[67] Butz, M.V., P.L. Lanzi, and S.W. Wilson, Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction. Evolutionary Computation, IEEE Transactions on, 2008. 12(3): p. 355-376.10.1109/TEVC.2007.903551Search in Google Scholar

[68] Hall, M., et al., The WEKA data mining software: an update. SIGKDD Explor. Newsl., 2009. 11(1): p. 10-18.Search in Google Scholar

[69] Lee, P.-M., Y. Teng, and T.-C. Hsiao. XCSF for prediction on emotion induced by image based on dimensional theory of emotion. in Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion. 2012. ACM.10.1145/2330784.2330842Search in Google Scholar

[70] Holland, J.H. and J.S. Reitman, Cognitive systems based on adaptive algorithms. SIGART Bull., 1977(63): p. 49-49.10.1145/1045343.1045373Search in Google Scholar

[71] Li, S. and B. Yang, Multifocus image fusion using region segmentation and spatial frequency. Image and Vision Computing, 2008. 26(7): p. 971-979.10.1016/j.imavis.2007.10.012Search in Google Scholar

[72] Leonard, H., et al., Brief Report: Developing Spatial Frequency Biases for Face Recognition in Autism andWilliams Syndrome. Journal of Autism and Developmental Disorders, 2011. 41(7): p. 968-973.10.1007/s10803-010-1115-7Search in Google Scholar

[73] John G, D., Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research, 1980. 20(10): p. 847-856.10.1016/0042-6989(80)90065-6Search in Google Scholar

[74] Webster, M.A. and R.L. De Valois, Relationship between spatial-frequency and orientation tuning of striate-cortex cells. J. Opt. Soc. Am. A, 1985. 2(7): p. 1124-1132.Search in Google Scholar

[75] Kobayashi, K., et al., Head and body sway in response to vertical visual stimulation. Acta Otolaryngologica, 2005. 125(8): p. 858-862.10.1080/0001648051003149816158533Search in Google Scholar

[76] Huang, N., et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. Lond. A, 1998. 454: p. 903-995.Search in Google Scholar

[77] Tay, P.C. AM-FM Image Analysis Using the Hilbert Huang Transform. in Image Analysis and Interpretation, 2008. SSIAI 2008. IEEE Southwest Symposium on. 2008.10.1109/SSIAI.2008.4512273Search in Google Scholar

[78] Caseiro, P., R. Fonseca-Pinto, and A. Andrade, Screening of obstructive sleep apnea using Hilbert–Huang decomposition of oronasal airway pressure recordings. Medical Engineering & Physics, 2010. 32(6): p. 561-568.10.1016/j.medengphy.2010.01.00820447855Search in Google Scholar

[79] Kaw, A. and E. Kalu, Numerical Methods with Applications2010: autarkaw.Search in Google Scholar

eISSN:
2083-2567
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Informatik, Künstliche Intelligenz, Datanbanken und Data Mining