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→Discriminative Dictionary Learning for Sparse Coding : A Batch Version and a Semi-Supervised Online Version
In the second part of the talk, we present an online, semi-supervised dictionary learning algorithm that is suitable when the size of the dataset is large and the labels are expensive to obtain. Similar to the previous work, our goal is to obtain a dictionary which is both representative and discriminative. Besides learning from labeled data, we also exploit the large amount of cheap, unlabeled training data to reinforce the representation power. An online framework makes the algorithm applicable to large-scale dataset.
In the second part of the talk, we present an online, semi-supervised dictionary learning algorithm that is suitable when the size of the dataset is large and the labels are expensive to obtain. Similar to the previous work, our goal is to obtain a dictionary which is both representative and discriminative. Besides learning from labeled data, we also exploit the large amount of cheap, unlabeled training data to reinforce the representation power. An online framework makes the algorithm applicable to large-scale dataset.
===Statistical Multi-Scale Decomposition Modeling of Texture and Applications===
Speaker: Prof. Mohand Said Allili, Assistant professor (University of Quebec/Canada): March 7, 2013
Texture modeling and representation has been the subject of interest of several research works in the last decades. This presentation will focus on texture representation using multi-scale decompositions. More specifically, a new statistical framework, based on finite mixtures of Generalized Gaussian (MoGG) distributions, will be presented to model the distribution of multi-scale wavelet/contourlet decomposition coefficients of texture images. After a brief review of the state of the art about wavelet/contourlet statistical modeling, details about parameter estimation of the MoGG model will be presented. Then, two applications will be shown for the proposed approach, namely: wavelet/contourlet-based texture classification and retrieval, and fabric texture defect detection. Experimental results with comparison to recent state of the art methods will be presented as well.
==Past Semesters==
==Past Semesters==
