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We propose a novel endoscopy video summarization approach based on unsupervised learning and feature discrimination. The proposed learning approach partitions the collection of video frames into homogeneous categories based on their visual and temporal descriptors. Also, it generates possibilistic memberships in order to represent the degree of typicality of each video frame within every category,...
We propose a novel image database categorization approach using robust unsupervised learning of finite generalized dirichlet mixture models with feature discrimination. The proposed algorithm is based on optimizing an objective function that associates two types of memberships with each data sample. The first one is the posterior probability and indicates how well a sample fits each estimated distribution...
We propose a novel possibilistic clustering algorithm based on robust modelling of the Generalized Dirichlet (GD) finite mixture. The algorithm generates two types of membership degrees. The first one is a posterior probability that indicates the degree to which the point fits the estimated distribution. The second membership represents the degree of “typicality” and is used to indentify and discard...
The performance of content-based image retrieval systems has proved to be inherently constrained by the used low-level features, and cannot give satisfactory results when the user's high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately,...
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