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EQ2425 Analysis and Search of Visual Data 7.5 credits

Course memo Autumn 2024-50240

Version 1 – 08/22/2024, 5:24:38 PM

Course offering

Autumn 2024-50240 (Start date 26 Aug 2024, English)

Language Of Instruction

English

Offered By

EECS/Intelligent Systems

Course memo Autumn 2024

Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Spring 2019

Content and learning outcomes

Course contents

This course introduces the principles of analysis and search of visual data, discusses fundamental concepts for similarity queries, and provides hands-on experience for selected popular visual search algorithms. The course includes topics on visual vocabularies and bags of words, image features, image feature detection and description, feature-based object recognition, classification and clustering, robust recognition, scalable recognition, compression of image feature descriptors, rate-constrained feature selection, mobile visual search, similarity queries on compressed data, identification rate for D-admissible systems, and compression schemes for similarity queries.

Intended learning outcomes

After passing this course, participants should be able to:

(1) Qualitatively describe the principles of analysis and search of visual data, i.e., visual vocabularies, image features, classification, recognition, and visual queries.

(2) Develop and implement (for example with MatLab) schemes for image feature extraction, classification, recognition, and mobile visual search.

(3) Analyze, compare, and explain design choices using the principles of analysis and search of visual data.

(4) Assess the performance of the developed query / recognition schemes quantitatively.

(5) Analyze given query problems, identify and explain the challenges, propose possible compression schemes, and explain the chosen design.

To achive higher grades, participants should also be able to:

- Solve given project problems well and submit clear, scientifically sound, and well-written reports.

Learning activities

Lectures to discuss new concepts and ideas, tutorials with problem solving sessions and group discussions, preparation assignments and group discussions, 3 group projects to develop individual software solutions, generate simulation results, explain the results, make an assessment that is scientifically sound and submit a clear technical report.

Preparations before course start

Recommended prerequisites

EQ2330 Image and Video Processing or equivalent

Literature

No information inserted

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • INL1 - Preparation assignments, 1.5 credits, Grading scale: P, F
  • PRO1 - Course projects, 3.0 credits, Grading scale: A, B, C, D, E, FX, F
  • TEN1 - Exam, 3.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The section below is not retrieved from the course syllabus:

Preparation assignments ( INL1 )

Course projects ( PRO1 )

Exam ( TEN1 )

Other requirements for final grade

(1) Preparation assignments, 1.5 ECTS (P/F): A few days before an exercise session, we hand out a short assignment to be solved individually before the session. During the session, you will discuss your prepared solution with your peers. The commented version of your prepared solution will be handed in at the end of the exercise session.

(2) Course projects, 3 ECTS (A-F): The projects will provide hands-on experience and should be performed in groups of two students.

(3) Written exam, 3 ECTS (A-F)

The final grade will be determined by the average of course projects and exam. The examiner reserves the right to adjust the weighting for each course round.

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Changes of the course before this course offering

Detailed information is given on the course website.

Round Facts

Start date

26 Aug 2024

Course offering

  • Autumn 2024-50240

Language Of Instruction

English

Offered By

EECS/Intelligent Systems

Contacts

Course Coordinator

Teachers

Teacher Assistants

Examiner