Abstract
Shape classification is employed for realizing image object identification and classification tasks. Most of the state-of-the-art approaches use sequential features extracted from contours to classify shapes, either directly, i.e., k-nearest neighbors (KNN), or through stochastic models, i.e., hidden Markov models (HMMs). Here, inspired by probability based metrics using Hilbert space embedding (HSE), we introduce a novel scheme for efficient shape classification. To this end, we highlight relevant curvature patterns from binary images towards a Kernel Adaptive Filtering (KAF)-based enhancement of the maximum mean discrepancy metric. Namely, we test the performance of our approach on the well-known MPEG-7 and 99-Shapes databases. Results show that our strategy can code relevant shape properties from binary images achieving competitive classification results.
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References
Bicego, M., Murino, V.: Investigating Hidden Markov models’ capabilities in 2D shape classification. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 281–286 (2004)
Chen, B., Zhao, S., Zhu, P., Principe, J.C.: Quantized Kernel least mean square algorithm. IEEE Trans. Neural Netw. Learn. Syst. 23(1), 22–32 (2012)
Keskin, C., Kıraç, F., Kara, Y.E., Akarun, L.: Hand pose estimation and hand shape classification using multi-layered randomized decision forests. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 852–863. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33783-3_61
Liu, W., Principe, J.C., Haykin, S.: Kernel Adaptive Filtering: A Comprehensive Introduction, vol. 57. John Wiley & Sons, New York (2011)
Luo, C., Ma, L.: Manifold regularized distribution adaptation for classification of remote sensing images. IEEE Access (2018)
Pun, C.M., Lin, C.: Geometric invariant shape classification using hidden markov model. In: 2010 International Conference on DICTA, pp. 406–410. IEEE (2010)
Rathi, Y., Vaswani, N., Tannenbaum, A., Yezzi, A.: Tracking deforming objects using particle filtering for geometric active contours. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1470–1475 (2007)
Sriperumbudur, B.K., Gretton, A., Fukumizu, K., Schölkopf, B., Lanckriet, G.R.: Hilbert space embeddings and metrics on probability measures. J. Mach. Learn. Res. 11, 1517–1561 (2010)
Zuluaga, C.D., Valencia, E.A., Álvarez, M.A., Orozco, Á.A.: A parzen-based distance between probability measures as an alternative of summary statistics in approximate bayesian computation. In: Murino, V., Puppo, E. (eds.) ICIAP 2015. LNCS, vol. 9279, pp. 50–61. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23231-7_5
Acknowledgments
Under grants provided by COLCIENCIAS project 1110-744-55958: “Desarrollo de un sistema de identificación de estructuras nerviosas en imágenes de ultrasonido para la asistencia de bloqueo de nervios periféricos". J.S. Blandon is partially funded by the project E6-18-09 (Vicerrectoria de Investigaciones, Innovación y Extensión) from Universidad Tecnológica de Pereira, and by COLCIENCIAS program 775: “Jóvenes Investigadores e Innovadores por la Paz 2017".
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Blandon, J.S., Valencia, C.K., Alvarez, A., Echeverry, J., Alvarez, M.A., Orozco, A. (2018). Shape Classification Using Hilbert Space Embeddings and Kernel Adaptive Filtering. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_28
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DOI: https://doi.org/10.1007/978-3-319-93000-8_28
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