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Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

H. Eidenberger, B. Klauninger:
"Similarity Assessment as a Dual Process Model of Counting and Measuring";
Vortrag: 5th International Conference on Pattern Recognition Applications and Methods, Rom; 24.02.2016 - 26.02.2016; in: "Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods", (2016), ISBN: 989-758-173-1; S. 141 - 147.



Kurzfassung deutsch:
Based on recent findings from the field of human similarity perception, we propose a dual process model
(DPM) of taxonomic and thematic similarity assessment which can be utilised in machine learning applications.
Taxonomic reasoning is related to predicate based measures (counting) whereas thematic reasoning is
mostly associated with metric distances (measuring). We suggest a procedure that combines both processes
into a single similarity kernel. For each feature dimension of the observational data, an optimal measure is
selected by a Greedy algorithm: A set of possible measures is tested, and the one that leads to improved classification
performance of the whole model is denoted. These measures are combined into a single SVM kernel
by means of generalisation (converting distances into similarities) and quantisation (applying predicate based
measures to interval scale data). We then demonstrate how to apply our model to a classification problem
of MPEG-7 features from a test set of images. Evaluation shows that the performance of the DPM kernel is
superior to those of the standard SVM kernels. This supports our theory that the DPM comes closer to human
similarity judgment than any singular measure, and it motivates our suggestion to employ the DPM not only
in image retrieval but also in related tasks.

Kurzfassung englisch:
Based on recent findings from the field of human similarity perception, we propose a dual process model
(DPM) of taxonomic and thematic similarity assessment which can be utilised in machine learning applications.
Taxonomic reasoning is related to predicate based measures (counting) whereas thematic reasoning is
mostly associated with metric distances (measuring). We suggest a procedure that combines both processes
into a single similarity kernel. For each feature dimension of the observational data, an optimal measure is
selected by a Greedy algorithm: A set of possible measures is tested, and the one that leads to improved classification
performance of the whole model is denoted. These measures are combined into a single SVM kernel
by means of generalisation (converting distances into similarities) and quantisation (applying predicate based
measures to interval scale data). We then demonstrate how to apply our model to a classification problem
of MPEG-7 features from a test set of images. Evaluation shows that the performance of the DPM kernel is
superior to those of the standard SVM kernels. This supports our theory that the DPM comes closer to human
similarity judgment than any singular measure, and it motivates our suggestion to employ the DPM not only
in image retrieval but also in related tasks.


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.5220/0005655801410147


Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.