|dc.description.abstract||Object classes are central to computer vision and have been the focus of substantial
research in the last fifteen years. This thesis addresses the tasks of localizing entire
objects in images (object class detection) and localizing their semantic parts (part detection).
We present four contributions, two for each task. The first two improve
existing object class detection techniques by using context and calibration. The other
two contributions explore semantic part detection in weakly-supervised settings.
First, the thesis presents a technique for predicting properties of objects in an image
based on its global appearance only. We demonstrate the method by predicting three
properties: aspect of appearance, location in the image and class membership. Overall,
the technique makes multi-component object detectors faster and improves their
The second contribution is a method for calibrating the popular Ensemble of Exemplar-
SVM object detector. Unlike the standard approach, which calibrates each Exemplar-
SVM independently, our technique optimizes their joint performance as an ensemble.
We devise an efficient optimization algorithm to find the global optimal solution of the
calibration problem. This leads to better object detection performance compared to
using independent calibration.
The third innovation is a technique to train part-based model of object classes using
data sourced from the web. We learn rich models incrementally. Our models encompass
the appearance of parts and their spatial arrangement on the object, specific to
each viewpoint. Importantly, it does not require any part location annotation, which is
one of the main limits to training many part detectors.
Finally, the last contribution is a study on whether semantic object parts emerge in
Convolutional Neural Networks trained for higher-level tasks, such as image classification.
While previous efforts studied this matter by visual inspection only, we perform
an extensive quantitative analysis based on ground-truth part location annotations. This
provides a more conclusive answer to the question.||en
|dc.publisher||The University of Edinburgh||en
|dc.relation.hasversion||Modolo, D., Vezhnevets, A., and Ferrari, V. (2015). Context forest for object class detection. In BMVC.||en
|dc.relation.hasversion||Modolo, D., Vezhnevets, A., Russakovsky, O., and Ferrari, V. (2015). Joint calibration of ensemble of exemplar svms. In CVPR.||en
|dc.title||Advances in detecting object classes and their semantic parts||en
|dc.type||Thesis or Dissertation||en
|dc.type.qualificationname||PhD Doctor of Philosophy||en