321 research outputs found
Learning preferences for large scale multi-label problems
Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable. The main contribution of this work is the proposal of a novel general online preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks
Automatic analysis of the video is one of most complex problems in the fields
of computer vision and machine learning. A significant part of this research
deals with (human) activity recognition (HAR) since humans, and the activities
that they perform, generate most of the video semantics. Video-based HAR has
applications in various domains, but one of the most important and challenging
is HAR in sports videos. Some of the major issues include high inter- and
intra-class variations, large class imbalance, the presence of both group
actions and single player actions, and recognizing simultaneous actions, i.e.,
the multi-label learning problem. Keeping in mind these challenges and the
recent success of CNNs in solving various computer vision problems, in this
work, we implement a 3D CNN based multi-label deep HAR system for multi-label
class-imbalanced action recognition in hockey videos. We test our system for
two different scenarios: an ensemble of binary networks vs. a single
-output network, on a publicly available dataset. We also compare our
results with the system that was originally designed for the chosen dataset.
Experimental results show that the proposed approach performs better than the
existing solution.Comment: Accepted to IEEE/ACIS SNPD 2018, 6 pages, 3 figure
Transductive Multi-label Zero-shot Learning
Zero-shot learning has received increasing interest as a means to alleviate
the often prohibitive expense of annotating training data for large scale
recognition problems. These methods have achieved great success via learning
intermediate semantic representations in the form of attributes and more
recently, semantic word vectors. However, they have thus far been constrained
to the single-label case, in contrast to the growing popularity and importance
of more realistic multi-label data. In this paper, for the first time, we
investigate and formalise a general framework for multi-label zero-shot
learning, addressing the unique challenge therein: how to exploit multi-label
correlation at test time with no training data for those classes? In
particular, we propose (1) a multi-output deep regression model to project an
image into a semantic word space, which explicitly exploits the correlations in
the intermediate semantic layer of word vectors; (2) a novel zero-shot learning
algorithm for multi-label data that exploits the unique compositionality
property of semantic word vector representations; and (3) a transductive
learning strategy to enable the regression model learned from seen classes to
generalise well to unseen classes. Our zero-shot learning experiments on a
number of standard multi-label datasets demonstrate that our method outperforms
a variety of baselines.Comment: 12 pages, 6 figures, Accepted to BMVC 2014 (oral
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