Partial Membership Latent Dirichlet Allocation
This addresses the issue of ambiguous region segmentation in images for computer vision applications, representing an incremental improvement over existing topic models.
The paper tackles the problem of crisp segmentation limitations in topic models for imagery by introducing a partial membership LDA model, which allows visual words to belong to multiple topics, enabling both crisp and soft segmentations as demonstrated in experiments on visual and sonar imagery.
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.