OVERVIEW

The course offers an introduction to state-of-the-art methods for visual data analysis. In particular it deals with image and video understanding.

AIMS AND CONTENT

AIMS AND LEARNING OUTCOMES

Students will be provided with an an overview of state-of-the-art methods for modeling and understanding the semantics of a scene. Students will get acquainted with the problem of representing the image content adaptively by means of shallow or deep computational models. Then it will address image classification and categorization problems. Possible extensions to depth and motion information will also be discussed.

Students will be involved in project activities.

TEACHING METHODS

Theoretical classes complemented by practical activities

SYLLABUS/CONTENT

Course content

• Introductory classes
  • Reviewing background knowledge from image processing (filters, features, histograms, color, ...) and machine learning (clustering and classification algorithms)
  • Problems formulation: image matching, image retrieval, image classification 
• Image representations
  • Early approaches: keypoints and bag-of-keypoints
  • Sparse coding over fixed over-complete dictionaries
  • Learning adaptive dictionaries (dictionary learning)
  • Coding-pooling approaches 
  • Deep architectures
• Additional topics: using context, dealing with temporal or depth information, data visualization issues
• Projects and study cases

RECOMMENDED READING/BIBLIOGRAPHY

material provided by the instructors (slides and papers)

TEACHERS AND EXAM BOARD

Exam Board

FRANCESCA ODONE (President)

LORENZO ROSASCO (President)

ANNALISA BARLA

NICOLETTA NOCETI

ALESSANDRO VERRI

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

• 50% theory (oral exam)
• 50% application (project+seminar)
• active participation (either in person or by actively participating to the course forum) will also be evaluated and will contribute to the final marking)

ASSESSMENT METHODS

• timely delivery of assignments
• active participation in class and on the online students forum (aulaweb)
• final project on a use-case (datathon-like) and presentation of the obtained results in a seminar
• oral exam

Exam schedule