Congratulations to our labmates and collaborators: Diandra Prioleau, Kiana Alikhademi, Armisha Roberts, Joshua Peeples, Alina Zare and Juan Gilbert! Their paper, “Application of Divisive Clustering for Reducing Bias in Imbalanced Data” was recently accepted to the the 2021 International Conference… Read More
Tag: clustering
APPLICATION OF DIVISIVE CLUSTERING FOR REDUCING BIAS IN IMBALANCED DATA
Abstract: A lack of diversity and representativeness within training data causes bias in the machine learning pipeline by influencing the performance of many machine learning models to favor the majority of samples that are most similar. It is necessary to… Read More
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
Abstract: Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloor environments. In this paper, we compare two possibilistic segmentation approaches. Possibilistic approaches allow… Read More
Quantitative Evaluation Metrics for Superpixel Segmentation
Abstract: Superpixel segmentation methods have been found to be increasingly valuable in image processing and analysis. Superpixel segmentation approaches have been used as a preprocessing step for a wide variety of image analysis tasks such as full scene segmentation, automated… Read More
Possibilistic fuzzy local information C-means with automated feature selection for seafloor segmentation
Abstract: The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS) imagery into distinct regions of the sea-floor. In this work, we investigate and present the results of an automated… Read More
Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation
Abstract: A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information. Partial Membership Latent Dirichlet Allocation is an effective approach for spectral unmixing while representing spectral… Read More
Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps
Abstract: A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC superpixel segmentation algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet… Read More
Partial Membership Latent Dirichlet Allocation for Soft Image Segmentation
Abstract: 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… Read More
Map-guided Hyperspectral Image Superpixel Segmentation Using Semi-supervised Partial Membership Latent Dirichlet Allocation
Abstract: Many superpixel segmentation algorithms which are suitable for the regular color images like images with three channels: red, green and blue (RGB images) have been developed in the literature. However, because of the high dimensionality of hyperspectral imagery, these… Read More
Semi-supervised Interactive Unmixing for Hyperspectral Image Analysis
Abstract: In the past several decades, hyperspectral imaging has drawn a lot of attention in the eld of remote sensing. Yet, due to low spatial resolutions of hyperspectral imagers, often the response from more than one surface material can be… Read More