2 research outputs found
Learning Mid-Level Representations for Visual Recognition
The objective of this thesis is to enhance visual recognition for objects and scenes
through the development of novel mid-level representations and appendent learning
algorithms. In particular, this work is focusing on category level recognition which
is still a very challenging and mainly unsolved task. One crucial component in visual
recognition systems is the representation of objects and scenes. However, depending on
the representation, suitable learning strategies need to be developed that make it possible
to learn new categories automatically from training data. Therefore, the aim of this thesis
is to extend low-level representations by mid-level representations and to develop suitable
learning mechanisms.
A popular kind of mid-level representations are higher order statistics such as
self-similarity and co-occurrence statistics. While these descriptors are satisfying the
demand for higher-level object representations, they are also exhibiting very large and ever
increasing dimensionality. In this thesis a new object representation, based on curvature
self-similarity, is suggested that goes beyond the currently popular approximation of
objects using straight lines. However, like all descriptors using second order statistics,
it also exhibits a high dimensionality. Although improving discriminability, the high
dimensionality becomes a critical issue due to lack of generalization ability and curse
of dimensionality. Given only a limited amount of training data, even sophisticated
learning algorithms such as the popular kernel methods are not able to suppress noisy or
superfluous dimensions of such high-dimensional data. Consequently, there is a natural
need for feature selection when using present-day informative features and, particularly,
curvature self-similarity. We therefore suggest an embedded feature selection method for
support vector machines that reduces complexity and improves generalization capability
of object models. The proposed curvature self-similarity representation is successfully
integrated together with the embedded feature selection in a widely used state-of-the-art
object detection framework.
The influence of higher order statistics for category level object recognition, is further
investigated by learning co-occurrences between foreground and background, to reduce
the number of false detections. While the suggested curvature self-similarity descriptor
is improving the model for more detailed description of the foreground, higher order
statistics are now shown to be also suitable for explicitly modeling the background.
This is of particular use for the popular chamfer matching technique, since it is prone
to accidental matches in dense clutter. As clutter only interferes with the foreground
model contour, we learn where to place the background contours with respect to the
foreground object boundary. The co-occurrence of background contours is integrated
into a max-margin framework. Thus the suggested approach combines the advantages of
accurately detecting object parts via chamfer matching and the robustness of max-margin
learning.
While chamfer matching is very efficient technique for object detection, parts are only
detected based on a simple distance measure. Contrary to that, mid-level parts and
patches are explicitly trained to distinguish true positives in the foreground from false
positives in the background. Due to the independence of mid-level patches and parts it
is possible to train a large number of instance specific part classifiers. This is contrary
to the current most powerful discriminative approaches that are typically only feasible
for a small number of parts, as they are modeling the spatial dependencies between
them. Due to their number, we cannot directly train a powerful classifier to combine
all parts. Instead, parts are randomly grouped into fewer, overlapping compositions that
are trained using a maximum-margin approach. In contrast to the common rationale of
compositional approaches, we do not aim for semantically meaningful ensembles. Rather
we seek randomized compositions that are discriminative and generalize over all instances
of a category. Compositions are all combined by a non-linear decision function which is
completing the powerful hierarchy of discriminative classifiers.
In summary, this thesis is improving visual recognition of objects and scenes, by
developing novel mid-level representations on top of different kinds of low-level
representations. Furthermore, it investigates in the development of suitable learning
algorithms, to deal with the new challenges that are arising form the novel object
representations presented in this work
Multi-Cue Pedestrian Classification With Partial Occlusion Handling
This paper presents a novel mixture-of-experts framework for pedestrian classification with partial occlusion handling. The framework involves a set of component-based expert classifiers trained on features derived from intensity, depth and motion. To handle partial occlusion, we compute expert weights that are related to the degree of visibility of the associated component. This degree of visibility is determined by examining occlusion boundaries, i.e. discontinuities in depth and motion. Occlusion-dependent component weights allow to focus the combined decision of the mixtureof-experts classifier on the unoccluded body parts. In experiments on extensive real-world data sets, with both partially occluded and non-occluded pedestrians, we obtain significant performance boosts over state-of-the-art approaches by up to a factor of four in reduction of false positives at constant detection rates. The dataset is made public for benchmarking purposes. 1