Partial Membership Latent Dirichlet Allocation
This addresses the need for more flexible image segmentation in domains like natural and SONAR imagery, though it is incremental as it extends existing LDA methods.
The paper tackled the problem of crisp segmentation limitations in topic models for imagery by proposing a partial membership LDA model, which allows for soft semantic segmentations in ambiguous regions, and demonstrated its capability on natural and SONAR image datasets.
Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp 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 associated parameter estimation algorithms. Experimental results on two natural image datasets and one SONAR image dataset show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability existing methods do not have.