Abstract: The application of Partial Membership Latent Dirichlet Allocation (PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a hyperspectral image analysis that accounts for spectral variability and incorporates spatial information through the use… Read More
Month: September 2016
Discriminative Multiple Instance Hyperspectral Target Characterization
Abstract: In this paper, two methods for multiple instance target characterization, MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target signature from imprecisely-labeled and mixed training data. In many applications, such as sub-pixel target detection in remotely-sensed… Read More
Partial Membership Latent Dirichlet Allocation for Image Segmentation
Abstract: 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… Read More
Multiple Instance Dictionary Learning using Functions of Multiple Instances
Abstract: A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that… Read More
Interested in joining the Machine Learning and Sensing Lab?
Thank you very much for your interest in our research group, The Machine Learning and Sensing Lab! I am always looking for talented and motivated students that are interested in conducting pattern recognition and machine learning research! University of Florida… Read More