Shape Classification Using Hilbert Space Embeddings and Kernel Adaptive Filtering View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-06-06

AUTHORS

J. S. Blandon , C. K. Valencia , A. Alvarez , J. Echeverry , M. A. Alvarez , A. Orozco

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. More... »

PAGES

245-251

Book

TITLE

Image Analysis and Recognition

ISBN

978-3-319-92999-6
978-3-319-93000-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-93000-8_28

DOI

http://dx.doi.org/10.1007/978-3-319-93000-8_28

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104412375


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