75 research outputs found
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
Multiple kernel learning (MKL) method is generally believed to perform better
than single kernel method. However, some empirical studies show that this is
not always true: the combination of multiple kernels may even yield an even
worse performance than using a single kernel. There are two possible reasons
for the failure: (i) most existing MKL methods assume that the optimal kernel
is a linear combination of base kernels, which may not hold true; and (ii) some
kernel weights are inappropriately assigned due to noises and carelessly
designed algorithms. In this paper, we propose a novel MKL framework by
following two intuitive assumptions: (i) each kernel is a perturbation of the
consensus kernel; and (ii) the kernel that is close to the consensus kernel
should be assigned a large weight. Impressively, the proposed method can
automatically assign an appropriate weight to each kernel without introducing
additional parameters, as existing methods do. The proposed framework is
integrated into a unified framework for graph-based clustering and
semi-supervised classification. We have conducted experiments on multiple
benchmark datasets and our empirical results verify the superiority of the
proposed framework.Comment: Accepted by IJCAI 2018, Code is availabl
Hybrid Bayesian Eigenobjects: Combining Linear Subspace and Deep Network Methods for 3D Robot Vision
We introduce Hybrid Bayesian Eigenobjects (HBEOs), a novel representation for
3D objects designed to allow a robot to jointly estimate the pose, class, and
full 3D geometry of a novel object observed from a single viewpoint in a single
practical framework. By combining both linear subspace methods and deep
convolutional prediction, HBEOs efficiently learn nonlinear object
representations without directly regressing into high-dimensional space. HBEOs
also remove the onerous and generally impractical necessity of input data
voxelization prior to inference. We experimentally evaluate the suitability of
HBEOs to the challenging task of joint pose, class, and shape inference on
novel objects and show that, compared to preceding work, HBEOs offer
dramatically improved performance in all three tasks along with several orders
of magnitude faster runtime performance.Comment: To appear in the International Conference on Intelligent Robots
(IROS) - Madrid, 201
Improving Image Classification with Location Context
With the widespread availability of cellphones and cameras that have GPS
capabilities, it is common for images being uploaded to the Internet today to
have GPS coordinates associated with them. In addition to research that tries
to predict GPS coordinates from visual features, this also opens up the door to
problems that are conditioned on the availability of GPS coordinates. In this
work, we tackle the problem of performing image classification with location
context, in which we are given the GPS coordinates for images in both the train
and test phases. We explore different ways of encoding and extracting features
from the GPS coordinates, and show how to naturally incorporate these features
into a Convolutional Neural Network (CNN), the current state-of-the-art for
most image classification and recognition problems. We also show how it is
possible to simultaneously learn the optimal pooling radii for a subset of our
features within the CNN framework. To evaluate our model and to help promote
research in this area, we identify a set of location-sensitive concepts and
annotate a subset of the Yahoo Flickr Creative Commons 100M dataset that has
GPS coordinates with these concepts, which we make publicly available. By
leveraging location context, we are able to achieve almost a 7% gain in mean
average precision
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