3 research outputs found
Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion
Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation to remote sensing synthetic aperture radar (SAR) and multispectral imaging (MSI) unsupervised land-cover categorization. Then, a novel pLSA-based image fusion approach is presented, which pursues to uncover multimodal feature patterns from SAR and MSI data in order to effectively fuse and categorize Sentinel-1 and Sentinel-2 remotely sensed data. Experiments conducted over two different data sets reveal the advantages of the proposed approach for unsupervised land-cover categorization tasks
Prior-based probabilistic latent semantic analysis for multimedia retrieval
Topic models have shown to be one of the most effective tools in Content-Based
Multimedia Retrieval (CBMR). However, the high computational learning cost together
with the huge expansion of multimedia collections limit the scalability of topic-based
CBMR systems in real-life multimedia applications. The present work pursues a twofold
objective. On the one hand, to study the effect of using clustering-based document reduction
schemes over standard topic models pLSA (probabilistic Latent Semantic Analysis)
and LDA (Latent Dirichlet Allocation). On the other hand, to develop a pLSA-based extension
oriented to integrate this reduction scheme within the own model in order to improve
the CBMR effectiveness. The experimental part of the work includes three different multimedia
databases, three ranking functions, four retrieval scenarios, three different numbers
of topics and ten document reduction levels. Experiments revealed that standard topic models
are highly sensitive to the document reduction level whereas the proposed model is able
to provide a competitive advantage within the content-based retrieval field