From Ontology to Visual Scene Understanding

Primary supervisor

Additional supervisors

  • Uli Sattler

Contact admissions office

Other projects with the same supervisor


  • Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Understanding visual scenes is one of the fundamental objectives of computer vision. A first step in scene understanding is the detection and recognition of objects from the input images, such as "person" or "bicycle", which is well-understood. A second step is the detection of visual relations between objects such as "person on bicycle" in the images, which is currently far more challenging. Machine learning models, such as neural networks, can in general be used to construct a mapping from an input image to a set of semantic relationships (or a scene graph) between the detected objects. However, success of state-of-the-art scene relation/graph extraction systems relies on the use of a very large amount of labeled image examples, called "ground truth".

This Ph.D. project will explore a different type of learning strategy, driven by information fusion and knowledge transfer. We plan to extend state-of-the-art example based learning to include: (1) processing and learning from existing knowledge in language domain and knowledge, and (2) integrating the learned knowledge with the image. We will analyse the trade-offs between purely ground-truth-based approaches and our extensions to include background knowledge, in particular for what kind of scenes what kind of knowledge can significantly reduce the need of ground truth.

This project lies in the fields of machine learning, computer vision, ontology and description logic.

We will consider applicants who have:
* Some existing knowledge in some of the above fields.
* Enthusiasm for research across the above research fields.
* An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
* Very good experience with computer programming of mathematical models and algorithms (in Python, Matlab or other platforms).
* Excellent report writing and presentation skills.
* Good ability to communicate with fellow students and colleagues.

Please, note that applicants must additionally satisfy the standard requirements for postgraduate studies at the University of Manchester, such as a first-class or high upper-second class (or an equivalent international qualification) and English language qualifications, as stated in the PGR guidelines.

Qualified applicants are strongly encouraged to informally contact the supervising academic Dr. Tingting Mu ( or Prof. Uli Sattler ( to discuss the application and possible research directions prior to applying.

Person specification

For information


Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
  • You will have good time management.


Applicants will be required to address the following.

  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?
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