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Daniel Leeds, Michael Tarr; Mixing hierarchical edge detection and medial axis models of object perception. Journal of Vision 2015;15(12):1095. doi: https://doi.org/10.1167/15.12.1095.
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© ARVO (1962-2015); The Authors (2016-present)
The nature of the visual representations underlying object perception in the brain is poorly understood. Past studies often have focused on the ability of one of two representational approaches to properly model biological object vision. Hierarchical edge models – pooling selected local edge information across space – have been used to help account for human neuroimaging data in response to photographs of 60 real-world objects (Leeds 2013). In contrast, medial axis models have been used successfully to probe mid- and high-level featural selectivities of single neurons in the cortical object perception pathway (Hung 2012). Both approaches, however, leave significant levels of unexplained variance in the neural data. To better model cortical object representations, we fit a weighted-sum mixture of models from the hierarchical and medial axis approaches – SIFT (Lowe 2004) and Shock Graph (Siddiqi 1999) – to neural data. We used fMRI to compare single and mixed-model responses with voxel population searchlight responses to a set of 60 object pictures. Representational distance matrices (Kriegeskorte 2008) were computed for each model combination and each voxel population, to serve as the basis of model-neural comparison. We found that weighted mixtures of the hierarchical and medial axis models exhibited varied results: from insignificant improvements (in lateral occipital cortex) to modest improvements (a 13% increased correlation in fusiform cortex) over either individual model in accounting for cortical representations in the brain regions associated with object vision. The fitted combination of the two models used weights of roughly consistent ratio across regions and subjects, reflective of the ratio of typical interobject distance values produced by the two model metrics. Each of the two models thus appears to account for a subset of the representational strategies realized in the human brain, in what is a sometimes mutually complementary and sometimes mutually equivalent manner.
Meeting abstract presented at VSS 2015
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