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Extension of higher order local autocorrelation features. (English) Zbl 1107.68469

Summary: This study investigates effective image features that are widely applicable in image analysis. We specifically address Higher order Local Autocorrelation (HLAC) features, which are used in various applications. The original HLAC features are restricted up to the second order and are represented by 25 mask patterns. We increase their orders up to eight and extract the extended HLAC features using 223 mask patterns. Furthermore, we create large mask patterns and construct multi-resolution features to support large displacement regions. In texture classification and face recognition, the proposed method outperformed Gaussian Markov random fields, Gabor features, and local binary pattern operator.

MSC:

68T10 Pattern recognition, speech recognition
68P15 Database theory

Software:

Outex
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] N. Otsu, T. Kurita, A new scheme for practical flexible and intelligent vision systems, in: Proceedings of the IAPR Workshop on Computer Vision, 1988, pp. 431-435.; N. Otsu, T. Kurita, A new scheme for practical flexible and intelligent vision systems, in: Proceedings of the IAPR Workshop on Computer Vision, 1988, pp. 431-435.
[2] T. Kurita, N. Otsu, T. Sato, A face recognition method using higher order local autocorrelation and multivariate analysis, in: Proceedings of the International Conference on Pattern Recognition, vol. 2, 1992, pp. 213-216.; T. Kurita, N. Otsu, T. Sato, A face recognition method using higher order local autocorrelation and multivariate analysis, in: Proceedings of the International Conference on Pattern Recognition, vol. 2, 1992, pp. 213-216.
[3] Goudail, F.; Lange, E.; Iwamoto, T.; Kyuma, K.; Otsu, N., Face recognition system using local autocorrelations and multiscale integration, IEEE Trans. Pattern Anal. Mach. Intell., 18, 10, 1024-1028 (1996)
[4] Kreutz, M.; Völpel, B.; Janssen, H., Scale-invariant image recognition based on higher order autocorrelation features, Pattern Recognition, 29, 1, 19-26 (1996)
[5] T. Kurita, S. Hayamizu, Gesture recognition using HLAC features of PARCOR images and HMM based recognizer, in: Proceedings of the International Conference on Automatic Face and Gesture Recognition, 1998, pp. 422-427.; T. Kurita, S. Hayamizu, Gesture recognition using HLAC features of PARCOR images and HMM based recognizer, in: Proceedings of the International Conference on Automatic Face and Gesture Recognition, 1998, pp. 422-427.
[6] K. Yamamoto, I. Ishii, A design of higher order auto-correlation vision chip, IEICE Trans. Inf. Sys. (D-II) J86-D-II(8) (2003) 1205-1211 (in Japanese).; K. Yamamoto, I. Ishii, A design of higher order auto-correlation vision chip, IEICE Trans. Inf. Sys. (D-II) J86-D-II(8) (2003) 1205-1211 (in Japanese).
[7] Chellappa, R.; Chatterjee, S., Classification of textures using Gaussian Markov random fields, IEEE Trans. Acoust. Speech Signal Process., 33, 959-963 (1985)
[8] Manjunath, B. S.; Ma, W. Y., Texture features for browsing and retrieval of image data, IEEE Trans. Pattern Anal. Mach. Intell., 18, 8, 837-842 (1996), \( \langle\) http://vision.ece.ucsb.edu/texture/software/\( \rangle \)
[9] Ojala, T.; Pietikäinen, M.; Mänpää, T., Multiresolution gray-scale and rotation-invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Mach. Intell., 24, 7, 971-987 (2002)
[10] V. Popovici, J.P. Thiran, Higher order autocorrelations for pattern classification, in: Proceedings of the International Conference on Image Processing, 2001, pp. 724-727.; V. Popovici, J.P. Thiran, Higher order autocorrelations for pattern classification, in: Proceedings of the International Conference on Image Processing, 2001, pp. 724-727.
[11] T. Ojala, T. MänpäÄ, M. Pietikäinen, J. Viertola, J. Kyllönen, S. Huovinen, Outex—new framework for empirical evaluation of texture analysis algorithms, in: Proceedings of the International Conference on Pattern Recognition, vol. 1, 2002, pp. \(701-706. \langle;\) http://www.outex.oulu.fi/outex.php \(\rangle;\); T. Ojala, T. MänpäÄ, M. Pietikäinen, J. Viertola, J. Kyllönen, S. Huovinen, Outex—new framework for empirical evaluation of texture analysis algorithms, in: Proceedings of the International Conference on Pattern Recognition, vol. 1, 2002, pp. \(701-706. \langle;\) http://www.outex.oulu.fi/outex.php \(\rangle;\)
[12] MeasTex Image Texture Database and Test Suite. \( \langle;\) http://www.cssip.uq.edu.au/meastex/meastex.html \(\rangle;\); MeasTex Image Texture Database and Test Suite. \( \langle;\) http://www.cssip.uq.edu.au/meastex/meastex.html \(\rangle;\)
[13] Department of Electrical and Information Engineering, Information Processing Laboratory, Machine Vision Group, University of Oulu. \( \langle;\) http://www.ee.oulu.fi/mvg/mvg.php \(\rangle;\); Department of Electrical and Information Engineering, Information Processing Laboratory, Machine Vision Group, University of Oulu. \( \langle;\) http://www.ee.oulu.fi/mvg/mvg.php \(\rangle;\)
[14] S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K.-R. Müller, Fisher discriminant analysis with kernels, in: Proceedings of the Neural Networks for Signal Processing IX, IEEE, 1999, pp. 41-48.; S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K.-R. Müller, Fisher discriminant analysis with kernels, in: Proceedings of the Neural Networks for Signal Processing IX, IEEE, 1999, pp. 41-48.
[15] Brodatz, P., Textures: A Photographic Album for Artists and Designers (1966), Dover: Dover New York
[16] AT & T database of faces. \( \langle;\) http://www.uk.research.att.com/facedatabase.html \(\rangle;\); AT & T database of faces. \( \langle;\) http://www.uk.research.att.com/facedatabase.html \(\rangle;\)
[17] T. Ahonen, A. Hadid, M. Pietikäinen, Face recognition with local binary patterns, in: Proceedings of the Eighth European Conference on Computer Vision Part I, 2004, pp. 469-481.; T. Ahonen, A. Hadid, M. Pietikäinen, Face recognition with local binary patterns, in: Proceedings of the Eighth European Conference on Computer Vision Part I, 2004, pp. 469-481. · Zbl 1098.68717
[18] A. Hadid, M. Pietikäinen, T. Ahonen, A discriminative feature space for detecting and recognizing faces, in: Proceedings of the Computer Vision and Pattern Recognition, vol. 2, 2004, pp. 797-804.; A. Hadid, M. Pietikäinen, T. Ahonen, A discriminative feature space for detecting and recognizing faces, in: Proceedings of the Computer Vision and Pattern Recognition, vol. 2, 2004, pp. 797-804.
[19] J. Li, S. Zhou, C. Shekhar, A comparison of subspace analysis for face recognition, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003, pp. 121-124.; J. Li, S. Zhou, C. Shekhar, A comparison of subspace analysis for face recognition, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003, pp. 121-124.
[20] K. Hotta, T. Kurita, T. Mishima, Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image, in: Proceedings of the Third IEEE International Conference on Face and Gesture Recognition, 1998, pp. 70-75.; K. Hotta, T. Kurita, T. Mishima, Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image, in: Proceedings of the Third IEEE International Conference on Face and Gesture Recognition, 1998, pp. 70-75.
[21] M. Suzuki, Y. Yaginuma, N. Osawa, Y. Sugimoto, Classification of 3D solid textures using 3D mask patterns, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, pp. 6342-6347.; M. Suzuki, Y. Yaginuma, N. Osawa, Y. Sugimoto, Classification of 3D solid textures using 3D mask patterns, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, pp. 6342-6347.
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