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Nonnegative Matrix Factorization for Segmentation Analysis

Nonnegative Matrix Factorization for Segmentation AnalysisBy: Roman Sandler

The conducted research project is concerned with image segmentation — one of the central problems of image analysis. A new model of segmented image is proposed and used to develop tools for analysis of image segmentations: image specific evaluation of segmentation algorithms' performance, extraction of image segment descriptors, and extraction of image segments. Prevalent seg-mentation models are typically based on the assumption of smoothness in the chosen image representation within the segments and contrast between them. The proposed model, unlike them, describes segmentations using image adaptive properties, which makes it relatively robust to context factors such as image quality or the presence of texture. The image repre-sentation in the proposed terms may be obtained in a fully unsupervised process and it does not require learning from other images.

 

 

 

Thesis: [PDF]