Categorization is a fundamental problem of many computer vision applications, e.g., image
classification, pedestrian detection and face recognition. The robustness of a categorization
system heavily relies on the quality of features, by which data are represented. The prior
arts of feature extraction can be concluded in different levels, which, in a bottom up order,
are low level features (e.g., pixels and gradients) and middle/high-level features (e.g., the
BoW model and sparse coding). Low level features can be directly extracted from images
or videos, while middle/high-level features are constructed upon low-level features, and are
designed to enhance the capability of categorization systems based on different considerations
(e.g., guaranteeing the domain-invariance and improving the discriminative power).
This thesis focuses on the study of visual feature learning. Challenges that remain in designing
visual features lie in intra-class variation, occlusions, illumination and view-point
changes and insufficient prior knowledge. To address these challenges, I present several
visual feature learning methods, where these methods cover the following sub-topics: (i)
I start by introducing a segmentation-based object recognition system. (ii) When training
data are insufficient, I seek data from other resources, which include images or videos in a
different domain, actions captured from a different viewpoint and information in a different
media form. In order to appropriately transfer such resources into the target categorization
system, four transfer learning-based feature learning methods are presented in this section,
where both cross-view, cross-domain and cross-modality scenarios are addressed accordingly.
(iii) Finally, I present a random-forest based feature fusion method for multi-view
action recognition.
