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From Ontology to Visual Scene Understanding

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.

Funding Notes

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.


Person specification

For information
•Candidates must hold a minimum of an upper Second Class UK Honours degree or international equivalent in a relevant science or engineering discipline.
•Candidates will be expected to comply with the University's policies and practices of equality, diversity and inclusion.
•Candidates must meet the School's minimum English Language requirement.


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?

How good is research at University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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