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.

Links:

Citation:

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. 

@Article{chen2016partial,
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},
}