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Image Classification using Cluster-Cooccurrence Matrices of Local Relational Features

Summary: Image Classification using Cluster-Cooccurrence Matrices
of Local Relational Features
Lokesh Setia, Alexandra Teynor, Alaa Halawani and Hans Burkhardt
Albert-Ludwigs-University Freiburg
79110 Freiburg im Breisgau, Germany
{setia, teynor, halawani, burkhardt} @informatik.uni-freiburg.de
Image classification systems have received a recent boost
from methods using local features generated over interest
points, delivering higher robustness against partial occlusion
and cluttered backgrounds. We propose in this paper to use
relational features calculated over multiple directions and
scales around these interest points. Furthermore, a very
important design issue is the choice of similarity measure to
compare the bags of local feature vectors generated by each
image, for which we propose a novel approach by computing
image similarity using cluster co-occurrence matrices of local
features. Excellent results are achieved for a widely used
medical image classification task, and ideas to generalize to
other tasks are discussed.


Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung


Collections: Computer Technologies and Information Sciences