RDFIA (M2)

Automne 2016 RDFIA

Bloc 1: Course 1: Computer Vision Introduction, Visual features and Bag of Word Image representation – Course1.pdf

Extra: -exo.pdf on image retrieval system with color, texture, shape and segmentation ArticleNetra.pdf -TDArtiCoding.pdf

Course 2 and 3: Classification: Datasets, benchmarks and evaluation, Linear classification (SVM) – Course2and3.pdf

Course 3 and 4: Use-Case for BoW – Course3and4.pdf – Course4annex.pdf Similarity, kernels

• TME1-2.pdf on SIFT descriptors TME3-4.pdf Bloc 2: Neural Nets for image classification: from MLP to CNN – Course5deep1.pdf – TP1 ConvNet – Course6deep2.pdf TP2: end of TP1 with complements and data – Course7deep3.pdf on Large/Deep ConvNet TP3 – Course8deep4.pdf Transfert and Deep Metric learning for image representation TP4

Bloc 3 : Stat. Learning: Risk and generalization, Fusion of pattern classifiers Extension of deep for segmentation, Visual QA

ARTICLES: Article on Spatial information in image representation SPM.pdf Beyond BoW pooling BossaNova.pdf Article on Fisher vector IFV.pdf Pyramid Kerenel PyramidMatchKernel.pdf article chatfield14return.pdf

Further reading: (some available at UPMC lib.) book1, book2, SzeliskiBook_draft.pdf livreAA.pdf, deep

Bloc 4 : cours Télécom