Visualization and GraphicsInteractionDept ICSFaculty of ScienceUU

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Course overview

The course is formed by several modules, starting with introductory notions and ending with advanced ones. The modules are described below. Note that a module is (typically) not 1-to-1 equivalent with a lecture.

Introduction

  • what is multimedia retrieval (MR)
  • types of MR data
  • the MR pipeline
  • example applications

Data representation

  • how MR data is represented
  • sampling and reconstruction
  • basis functions, grids, interpolation
  • types of MR attributes (scalar, vector, color, tensor)

Perception in MR

  • why human perception is important in MR
  • challenges of modeling perception
  • Gestalt principles
  • visual illusions

Feature extraction

  • concept of features and descriptors
  • taxonomy of features
  • features for image, 2D shape, and 3D shape data
  • feature extraction challenges and good practice

Matching

  • concepts of similarity and distance
  • different types of distance functions
  • distance transforms

Scalability

  • scalability challenge in MR
  • solutions: indexing, k-nearest neighbors, clustering
  • relation of MR with Machine Learning

Presentation

  • how to query and visually explore MR data
  • dimensionality reduction for exploration
  • dimensionality reduction for querying

Evaluation

  • quality concept in MR
  • quality metrics (FP, TP, F-score, AUC)
  • quality measurement in practice