|dc.description.abstract||This thesis explores the use of invariant features for learning sprites from image sequences, and
for recognising object categories in images.
A popular framework for the interpretation of image sequences is the layers or sprite model
of e.g.Wang and Adelson (1994), Irani et al. (1994). Jojic and Frey (2001) provide a generative
probabilistic model framework for this task, but their algorithm is slow as it needs to search
over discretised transformations (e.g. translations, or affines) for each layer. We show that by
using invariant features (e.g. Lowe’s SIFT features) and clustering their motions we can reduce
or eliminate the search and thus learn the sprites much faster. The algorithm is demonstrated
on example image sequences.
We introduce the Generative Template of Features (GTF), a parts-based model for visual
object category detection. The GTF consists of a number of parts, and for each part there is
a corresponding spatial location distribution and a distribution over ‘visual words’ (clusters of
invariant features). We evaluate the performance of the GTF model for object localisation as
compared to other techniques, and show that such a relatively simple model can give state-of-
the-art performance. We also discuss the connection of the GTF to Hough-transform-like
methods for object localisation.||en