Table of Contents




Welcome to the course website for “Multimedia Data Mining” SoSe 2017! This lecture gives an introduction to social multimedia processing and extracting information from images, videos, and other multimedia content. The focus will be on retrieval, search, and filtering of concepts, objects, and scenes.


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Schedule

Lecture schedule:

block date lecturer content details slides & demos exercise sheets & materials
1 18.04.17 Damian Borth Introduction course formalities, introduction slides -
2 25.04.17 Damian Borth Multimedia Information Systems definitions, setup, labels, benchmarking slides sheet
3 02.05.17 Damian Borth No lecture - - Ipython/Jupyter Installation link
4 09.05.17 Marco Schreyer Pattern Recognition (supervised) basic decision theory, classification, classifier combination slides sheet material NumPy Website NumPy Tutorial Ex2IPythonNotebook
5 16.05.17 Damian Borth Pattern Recognition (unsupervised) clustering, dimensionality reduction slides -
6 23.05.17 Damian Borth Text Retrieval document retrieval, retrieval models, relevance feedback, data structures slides -
7 30.05.17 Damian Borth Image Representation definitions, features and their properties, invariance, global features, local features slides sheet, URL list, CAFFE website, MNIST Tutorial, CAFFE website, K-Means-IPython
8 06.06.17 Christian Schulze Similarity Search applications, prototypical architecture, distance measures, object search slides -
9 13.06.17 Damian Borth Visual Recognition and Image Classification 1 terminology, applications, face detection, face recognition, object recognition, visual words slides sheet, material
10 20.06.17 Damian Borth Visual Recognition and Image Classification 2 Bag-of-Words, Support Vector Machine (SVM), Spatial Location Extension, Concept Vocabularies slides -
11 27.06.17 Christian Schulze Video Analysis shot boundary detection, keyframe extraction, motion estimation & applications, audio analysis slides -
12 04.07.17 Marco Schreyer Deep Learning 1 - DL 101 and Autoencoders end-to-end learning, neural networks, autoencoder slides sheet, material
13 11.07.17 Damian Borth Deep Learning 2 Convolutional Neural Networks, Long-short Term Memory Networks, Generative Adversarial Networks slides -
14 18.07.17 Damian Borth Deep Learning 3 Visual Sentiment Analysis, AdjectiveNounPairs, DeepSentiBank slides Q&A Session