Use this URL to cite or link to this record in EThOS:
Title: Weakly supervised learning of visual semantic attributes
Author: Hanwell, David
ISNI:       0000 0004 5921 7693
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2014
Availability of Full Text:
Access from EThOS:
There are at present many billions of images on the internet, only a fraction of which are labelled according to their semantic content. To automatically provide labels for the rest, models of visual semantic concepts must be created. Such models are traditionally trained using images which have been manually acquired, segmented, and labelled. In this thesis, we submit that such models can be learned automatically using those few images which have already been labelled, either directly by their creators, or indirectly by their associated text. Such imagery can be acquired easily, cheaply, and in large quantities, using web image searches. Though there has been some work towards learning from such weakly labelled data, all methods yet proposed require more than a minimum of human effort. In this thesis we put forth a number of methods for reliably learning models of visual semantic attributes using only the raw, unadulterated results of web image searches. The proposed methods do not require any human input beyond specifying the names of the attributes to be learned. We also present means of identifying and localising learned attributes in challenging, real-world images. Our methods are of a probabilistic nature, and make extensive use of multivariate Gaussian mixture models to represent both data and learned models. The contributions of this thesis also include several tools for acquiring and comparing these distributions, including a novel clustering algorithm. We apply our weakly supervised learning methods to the training of models of a variety of visual semantic attributes including colour and pattern terms. Detection and localization of the learned attributes in unseen realworld images is demonstrated, and both quantitative and qualitative results are presented. We compare against other work, including both general methods of weakly supervised learning, and more attribute specific methods. We apply our learning methods to the training sets of previous works, and assess their performance on the test sets used by other authors. Our results show that our methods give better results than the current state of the art.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available