Partial Membership Latent Dirichlet Allocation for Soft Image Segmentation


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.


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C. Chen, A. Zare, H. Trinh, G. Omotara, J. T. Cobb, and P. Lagaunne, “Partial Membership Latent Dirichlet Allocation,” IEEE Trans. Image Process.vol. 26, pp. 5590-5602, Dec. 2017. 
Title = {Partial Membership Latent Dirichlet Allocation for Soft Image Segmentation},
Author = {Chen, C. and Zare, A. and Trinh, H. and Omotara, G. and Cobb, J. T. and Lagaunne, P. },
Journal = {IEEE Trans. Image Proc.},
Year = {2017},
Month = {Dec.},
Volume = {26},
Number = {12},
Pages = {5590-5602},
Doi = {10.1109/TIP.2017.2736419},