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
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
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.
