Partial Membership Latent Dirichlet Allocation
Abstract
Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics. To address this, we present a partial membership latent Dirichlet allocation (PM-LDA) model and an associated parameter estimation algorithm. This model can be useful for imagery where a visual word may be a mixture of multiple topics. Experimental results on visual and sonar imagery show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability previous topic modeling methods do not have.
Cite
@article{arxiv.1612.08936,
title = {Partial Membership Latent Dirichlet Allocation},
author = {Chao Chen and Alina Zare and Huy Trinh and Gbeng Omotara and J. Tory Cobb and Timotius Lagaunne},
journal= {arXiv preprint arXiv:1612.08936},
year = {2016}
}
Comments
Version 1, Sent for Review. arXiv admin note: substantial text overlap with arXiv:1511.02821