Comparing computational and human measures of visual similarity
There have been many attempts to quantify visual similarity within different categories of objects, with a view to using such measures to predict impaired recognition performance. Although many studies have linked measures of visual similarity to behavioral outcomes associated with object recognition, there has been little research on whether these measures are associated with human ratings of perceived similarity. In this work, we compare similarity measures extracted from Principal Component Analysis, Isometric Feature Mapping and wavelets representations with ratings of human subjects. Our results show that features extracted by calculating the standard deviation of wavelet coefficients provides the closest fit to the human rating data of all the methods we applied here.
Item Type | Book Section |
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Additional information | "Electronic version of an article published as [Gale, T.M., Sun, Yi., Adams, R. and Davey, N. (2005) ' Comparing computational and human measures of visual similarity.' Modeling Language, Cognition and Action - Procs of the 9th Neural Computation and Psychology Workshop pp.397-401] 10.1142/9789812701886_0042 copyright World Scientific Publishing Company , Original paper can be found at: http://eproceedings.worldscinet.com/9789812701886/9789812701886_0042.html " |
Date Deposited | 15 May 2025 16:26 |
Last Modified | 06 Jun 2025 23:11 |
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