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.

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.

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.

http://dx.doi.org/10.5220/0005655801410147

Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.