1,122 research outputs found
Hypergraph Modelling for Geometric Model Fitting
In this paper, we propose a novel hypergraph based method (called HF) to fit
and segment multi-structural data. The proposed HF formulates the geometric
model fitting problem as a hypergraph partition problem based on a novel
hypergraph model. In the hypergraph model, vertices represent data points and
hyperedges denote model hypotheses. The hypergraph, with large and
"data-determined" degrees of hyperedges, can express the complex relationships
between model hypotheses and data points. In addition, we develop a robust
hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF
can effectively and efficiently estimate the number of, and the parameters of,
model instances in multi-structural data heavily corrupted with outliers
simultaneously. Experimental results show the advantages of the proposed method
over previous methods on both synthetic data and real images.Comment: Pattern Recognition, 201
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
In this paper, we propose a novel geometric model fitting method, called
Mode-Seeking on Hypergraphs (MSH),to deal with multi-structure data even in the
presence of severe outliers. The proposed method formulates geometric model
fitting as a mode seeking problem on a hypergraph in which vertices represent
model hypotheses and hyperedges denote data points. MSH intuitively detects
model instances by a simple and effective mode seeking algorithm. In addition
to the mode seeking algorithm, MSH includes a similarity measure between
vertices on the hypergraph and a weight-aware sampling technique. The proposed
method not only alleviates sensitivity to the data distribution, but also is
scalable to large scale problems. Experimental results further demonstrate that
the proposed method has significant superiority over the state-of-the-art
fitting methods on both synthetic data and real images.Comment: Proceedings of the IEEE International Conference on Computer Vision,
pp. 2902-2910, 201
A General Two-Step Approach to Learning-Based Hashing
Most existing approaches to hashing apply a single form of hash function, and
an optimization process which is typically deeply coupled to this specific
form. This tight coupling restricts the flexibility of the method to respond to
the data, and can result in complex optimization problems that are difficult to
solve. Here we propose a flexible yet simple framework that is able to
accommodate different types of loss functions and hash functions. This
framework allows a number of existing approaches to hashing to be placed in
context, and simplifies the development of new problem-specific hashing
methods. Our framework decomposes hashing learning problem into two steps: hash
bit learning and hash function learning based on the learned bits. The first
step can typically be formulated as binary quadratic problems, and the second
step can be accomplished by training standard binary classifiers. Both problems
have been extensively studied in the literature. Our extensive experiments
demonstrate that the proposed framework is effective, flexible and outperforms
the state-of-the-art.Comment: 13 pages. Appearing in Int. Conf. Computer Vision (ICCV) 201
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Supervised hashing aims to map the original features to compact binary codes
that are able to preserve label based similarity in the Hamming space.
Non-linear hash functions have demonstrated the advantage over linear ones due
to their powerful generalization capability. In the literature, kernel
functions are typically used to achieve non-linearity in hashing, which achieve
encouraging retrieval performance at the price of slow evaluation and training
time. Here we propose to use boosted decision trees for achieving non-linearity
in hashing, which are fast to train and evaluate, hence more suitable for
hashing with high dimensional data. In our approach, we first propose
sub-modular formulations for the hashing binary code inference problem and an
efficient GraphCut based block search method for solving large-scale inference.
Then we learn hash functions by training boosted decision trees to fit the
binary codes. Experiments demonstrate that our proposed method significantly
outperforms most state-of-the-art methods in retrieval precision and training
time. Especially for high-dimensional data, our method is orders of magnitude
faster than many methods in terms of training time.Comment: Appearing in Proc. IEEE Conf. Computer Vision and Pattern
Recognition, 2014, Ohio, US
A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences
Given an image sequence of a scene consisting of multiple rigidly moving objects, multi-body structure-and-motion (MSaM) is the task to segment the image feature tracks into the different rigid objects and compute the multiple-view geometry of each object. We present a framework for multibody structure-and-motion based on model selection. In a recover-and-select procedure, a redundant set of hypothetical scene motions is generated. Each subset of this pool of motion candidates is regarded as a possible explanation of the image feature tracks, and the most likely explanation is selected with model selection. The framework is generic and can be used with any parametric camera model, or with a combination of different models. It can deal with sets of correspondences, which change over time, and it is robust to realistic amounts of outliers. The framework is demonstrated for different camera and scene model
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