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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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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