5,444 research outputs found
Matching Image Sets via Adaptive Multi Convex Hull
Traditional nearest points methods use all the samples in an image set to
construct a single convex or affine hull model for classification. However,
strong artificial features and noisy data may be generated from combinations of
training samples when significant intra-class variations and/or noise occur in
the image set. Existing multi-model approaches extract local models by
clustering each image set individually only once, with fixed clusters used for
matching with various image sets. This may not be optimal for discrimination,
as undesirable environmental conditions (eg. illumination and pose variations)
may result in the two closest clusters representing different characteristics
of an object (eg. frontal face being compared to non-frontal face). To address
the above problem, we propose a novel approach to enhance nearest points based
methods by integrating affine/convex hull classification with an adapted
multi-model approach. We first extract multiple local convex hulls from a query
image set via maximum margin clustering to diminish the artificial variations
and constrain the noise in local convex hulls. We then propose adaptive
reference clustering (ARC) to constrain the clustering of each gallery image
set by forcing the clusters to have resemblance to the clusters in the query
image set. By applying ARC, noisy clusters in the query set can be discarded.
Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method
outperforms single model approaches and other recent techniques, such as Sparse
Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant
Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Structured Learning of Tree Potentials in CRF for Image Segmentation
We propose a new approach to image segmentation, which exploits the
advantages of both conditional random fields (CRFs) and decision trees. In the
literature, the potential functions of CRFs are mostly defined as a linear
combination of some pre-defined parametric models, and then methods like
structured support vector machines (SSVMs) are applied to learn those linear
coefficients. We instead formulate the unary and pairwise potentials as
nonparametric forests---ensembles of decision trees, and learn the ensemble
parameters and the trees in a unified optimization problem within the
large-margin framework. In this fashion, we easily achieve nonlinear learning
of potential functions on both unary and pairwise terms in CRFs. Moreover, we
learn class-wise decision trees for each object that appears in the image. Due
to the rich structure and flexibility of decision trees, our approach is
powerful in modelling complex data likelihoods and label relationships. The
resulting optimization problem is very challenging because it can have
exponentially many variables and constraints. We show that this challenging
optimization can be efficiently solved by combining a modified column
generation and cutting-planes techniques. Experimental results on both binary
(Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC
2012) segmentation datasets demonstrate the power of the learned nonlinear
nonparametric potentials.Comment: 10 pages. Appearing in IEEE Transactions on Neural Networks and
Learning System
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
Tackling pattern recognition problems in areas such as computer vision,
bioinformatics, speech or text recognition is often done best by taking into
account task-specific statistical relations between output variables. In
structured prediction, this internal structure is used to predict multiple
outputs simultaneously, leading to more accurate and coherent predictions.
Structural support vector machines (SSVMs) are nonprobabilistic models that
optimize a joint input-output function through margin-based learning. Because
SSVMs generally disregard the interplay between unary and interaction factors
during the training phase, final parameters are suboptimal. Moreover, its
factors are often restricted to linear combinations of input features, limiting
its generalization power. To improve prediction accuracy, this paper proposes:
(i) Joint inference and learning by integration of back-propagation and
loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM
factors to neural networks that form highly nonlinear functions of input
features. Image segmentation benchmark results demonstrate improvements over
conventional SSVM training methods in terms of accuracy, highlighting the
feasibility of end-to-end SSVM training with neural factors
Methods for Learning Structured Prediction in Semantic Segmentation of Natural Images
Automatic segmentation and recognition of semantic classes in natural images is an important open problem in computer vision. In this work, we investigate three different approaches to recognition: without supervision, with supervision on level of images, and with supervision on the level of pixels. The thesis comprises three parts. The first part introduces a clustering algorithm that optimizes a novel information-theoretic objective function. We show that the proposed algorithm has clear advantages over standard algorithms from the literature on a wide array of datasets. Clustering algorithms are an important building block for higher-level computer vision applications, in particular for semantic segmentation. The second part of this work proposes an algorithm for automatic segmentation and recognition of object classes in natural images, that learns a segmentation model solely from annotation in the form of presence and absence of object classes in images. The third and main part of this work investigates one of the most popular approaches to the task of object class segmentation and semantic segmentation, based on conditional random fields and structured prediction. We investigate several learning algorithms, in particular in combination with approximate inference procedures. We show how structured models for image segmentation can be learned exactly in practical settings, even in the presence of many loops in the underlying neighborhood graphs. The introduced methods provide results advancing the state-of-the-art on two complex benchmark datasets for semantic segmentation, the MSRC-21 Dataset of RGB images and the NYU V2 Dataset or RGB-D images of indoor scenes. Finally, we introduce a software library that al- lows us to perform extensive empirical comparisons of state-of-the-art structured learning approaches. This allows us to characterize their practical properties in a range of applications, in particular for semantic segmentation and object class segmentation.Methoden zum Lernen von Strukturierter Vorhersage in Semantischer Segmentierung von Natürlichen Bildern Automatische Segmentierung und Erkennung von semantischen Klassen in natür- lichen Bildern ist ein wichtiges offenes Problem des maschinellen Sehens. In dieser Arbeit untersuchen wir drei möglichen Ansätze der Erkennung: ohne Überwachung, mit Überwachung auf Ebene von Bildern und mit Überwachung auf Ebene von Pixeln. Diese Arbeit setzt sich aus drei Teilen zusammen. Im ersten Teil der Arbeit schlagen wir einen Clustering-Algorithmus vor, der eine neuartige, informationstheoretische Zielfunktion optimiert. Wir zeigen, dass der vorgestellte Algorithmus üblichen Standardverfahren aus der Literatur gegenüber klare Vorteile auf vielen verschiedenen Datensätzen hat. Clustering ist ein wichtiger Baustein in vielen Applikationen des machinellen Sehens, insbesondere in der automatischen Segmentierung. Der zweite Teil dieser Arbeit stellt ein Verfahren zur automatischen Segmentierung und Erkennung von Objektklassen in natürlichen Bildern vor, das mit Hilfe von Supervision in Form von Klassen-Vorkommen auf Bildern in der Lage ist ein Segmentierungsmodell zu lernen. Der dritte Teil der Arbeit untersucht einen der am weitesten verbreiteten Ansätze zur semantischen Segmentierung und Objektklassensegmentierung, Conditional Random Fields, verbunden mit Verfahren der strukturierten Vorhersage. Wir untersuchen verschiedene Lernalgorithmen des strukturierten Lernens, insbesondere im Zusammenhang mit approximativer Vorhersage. Wir zeigen, dass es möglich ist trotz des Vorhandenseins von Kreisen in den betrachteten Nachbarschaftsgraphen exakte strukturierte Modelle zur Bildsegmentierung zu lernen. Mit den vorgestellten Methoden bringen wir den Stand der Kunst auf zwei komplexen Datensätzen zur semantischen Segmentierung voran, dem MSRC-21 Datensatz von RGB-Bildern und dem NYU V2 Datensatz von RGB-D Bildern von Innenraum-Szenen. Wir stellen außerdem eine Software-Bibliothek vor, die es erlaubt einen weitreichenden Vergleich der besten Lernverfahren für strukturiertes Lernen durchzuführen. Unsere Studie erlaubt uns eine Charakterisierung der betrachteten Algorithmen in einer Reihe von Anwendungen, insbesondere der semantischen Segmentierung und Objektklassensegmentierung
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