Similarity Assessment as a Dual Process Model of Counting and Measuring

Abstract

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.