[1]JIANG Ying,WANG Yanjiang.Application of REM memory model in image recognition and classification[J].CAAI Transactions on Intelligent Systems,2017,12(3):310-317.[doi:10.11992/tis.201605010]
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Application of REM memory model in image recognition and classification

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 3 Page number: 310-317 Column: 学术论文—机器感知与模式识别 Public date: 2017-06-25
Title:
Application of REM memory model in image recognition and classification
Author(s):
JIANG Ying;  WANG Yanjiang
College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
Keywords:
image recognition; memory modeling; HOG feature; LBP feature; Bayesian decision
CLC:
TP391
DOI:
10.11992/tis.201605010
Abstract:
We attempt to combine a memory model with image learning and recognition and to research the application of the REM model in image recognition and classification. An image feature vector was obtained by histograms of oriented gradients (HOG) and local binary pattern (LBP) operators; every component of a feature vector was copied with a certain probability, allowing for an error-prone copy of the studied vector. Finally, Bayesian decision theory was applied for calculating the average likelihood ratio between the feature vector of the probe image and that of the studied image set. The value of the ratio was used to decide whether the probe image had been studied.Experimental results demonstrate that the proposed method can gain a good recognition effect not only for the classification of the same object with small rotation angles but also for the recognition of the same category object. Moreover, the false rate is far lower than that of other classification methods.
References:
[1] XU Y, ZHANG B, ZHONG Z. Multiple representations and sparse representation for image classification[J]. Pattern recognition letters, 2015, 68: 9-14.
[2] HELLMAN M E. The nearest neighbor classification rule with a reject option[J]. IEEE transactions on systems science and cybernetics, 1970, 6(3): 179-185.
[3] DENOEUX T. A neural network classifier based on Dempster-Shafer theory[J]. IEEE transactions on systems man and cybernetics-Part A systems and humans, 2010, 30(2): 131-150.
[4] DAVTALAB R, DEZFOULIAN M H, MANSOORIZADEH M. Multi-level fuzzy min-max neural network classifier[J]. IEEE transactions on neural networks and learning systems, 2014, 25(3): 470-482.
[5] CHANG C C, LIN C J. LIBSVM: a library for support vector machines[J].ACM transactions on intelligent systems and technology, 2011, 2(3): 389-396.
[6] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. Computer science, 2014.
[7] HUANG G B. What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle[J]. Cognitive computation, 2015, 7(3) 263-278.
[8] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE transactions on pattern analysis and machine intelligence, 2008, 31(2) 210-227.
[9] ZHANG L, YANG M, FENG X. Sparse representation or collaborative representation: Which helps face recognition[C]//International Conference on Computer Vision, 2011: 471-478.
[10] YANG M, ZHANG L YANG J, ZHANG D. Robust sparse coding for face recognition[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2011, 42(7): 625-632.
[11] SCHOLKOPF B, PLATT J, HOFMANN T. Sparse representation for signal classification[J]. Advances in neural information processing systems, 2007, 19: 609-616.
[12] WANG Y. Formal description of the cognitive process of memorization[M]//Transactions on Computational Science V. Springer Berlin Heidelberg, 2009: 81-98.
[13] WANG Y, CHIEW V. On the cognitive process of human problem solving[J]. Cognitive systems research, 2010, 11(1): 81-92.
[14] MURDOCK B B. A theory of the storage and retrieval of item and associative information[J]. Psychological review, 1982, 89(6): 609-626.
[15] RAAIJMAKERS J G W, SHIFFRIN R M. SAM: a theory of probabilistic search of associative memory[J]. Psychology of learning and motivation, 1980, 14: 207-262.
[16] HINTZMAN D L. Judgments of frequency and recognition memory in a multiple-trace model[J]. Psychological review, 1988, 95(4): 528-551.
[17] SHIFFRIN R M, STEYVERS M. A model for recognition memory: REM-retrieving effectively from memory[J]. Psychonomic bulletin and review, 1997, 4(2): 145-166.
[18] STARN J J, WHITE C N, RATCLIFF R. A direct test of the differentiation mechanism: REM, BCDMEM, and the strength-based mirror effect in recognition memory[J]. Journal of memory and language, 2010, 63(1): 18-34.
[19] COX G E, SHIFFRIN R M. Criterion Setting and the dynamics of recognition memory[J]. Topics in cognitive science, 2012, 4(1): 135-150.
[20] CRISS A H,MCCLELLAND J L. Differentiating the differentiation models: a comparison of the retrieving effectively from memory model (REM) and the subjective likelihood model (SLiM)[J]. Journal of memory and language, 2006, 55(4): 447-460.
[21] MONTENEGRO M, MYUNG J I, PITT M A. Analytical expressions for the REM model of recognition memory[J]. Journal of mathematical psychology, 2014, 60(3): 23-28.
[22] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Computer Vision and Pattern Recognition. IEEE Computer Society Conference, on, 2005: 886-893.
[23] OJALA T, HARWOOD I. A Comparative study of texture measures with classification based on feature distributions[J]. Pattern recognition, 1996, 29(1): 51-59.
[24] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision, 2004, 60(2):91-110.
[25] BAY H, TUYTELAARS T, VAN GOOL L. SURF: speeded up robust features[J]. Computer vision and image understanding, 2006, 110(3): 404-417.
[26] PIETIKAINEN M, OJALA T, XU Z. Rotation-invariant texture classification using feature distributions[J]. Pattern recognition, 2000, 33(1): 43-52.
[27] OJALA T, PIETIKAINEN, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(7): 971-987.
[28] NENE A S, NAYAR S K, MURASE H. Columbia object image library (COIL-20)[R]. CUCS-005-96, 1996.
[29] GRIFFIN G, HOLUB A, PERONA P. Caltech-256 object category dataset[R]. Pasadena: California Institute of Technology, 2007.
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