Learned Shape-Tailored Descriptors for Segmentation

Naeemullah Khan, Ganesh Sundaramoorthi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We address the problem of texture segmentation by grouping dense pixel-wise descriptors. We introduce and construct learned Shape-Tailored Descriptors that aggregate image statistics only within regions of interest to avoid mixing statistics of different textures, and that are invariant to complex nuisances (e.g., illumination, perspective and deformations). This is accomplished by training a neural network to discriminate base shape-tailored descriptors of oriented gradients at various scales. These descriptors are defined through partial differential equations to obtain data at various scales in arbitrarily shaped regions. We formulate and optimize a joint optimization problem in the segmentation and descriptors to discriminate these base descriptors using the learned metric, equivalent to grouping learned descriptors. Experiments on benchmark datasets show that the descriptors learned on a small dataset of segmented images generalize well to unseen textures in other datasets, showing the generic nature of these descriptors. We also show state-of-the-art results on texture segmentation benchmarks.
Original languageEnglish (US)
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages666-674
Number of pages9
ISBN (Print)9781538664209
DOIs
StatePublished - Dec 18 2018

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