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Chao Chen; Zare, Alina; Trinh, Huy N.; Omotara, Gbenga O.; Cobb, James Tory; Lagaunne, Timotius A.
IEEE transactions on image processing, 12/2017, Letnik: 26, Številka: 12Journal Article
Topic models e.g., probabilistic latent semantic analysis, latent Dirichlet allocation (LDA), and supervised LDA 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 LDA (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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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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