24,233 research outputs found
Class-based probability estimation using a semantic hierarchy
This article concerns the estimation of a particular kind of probability, namely, the probability of a noun sense appearing as a particular argument of a predicate. In order to overcome the accompanying sparse-data problem, the proposal here is to define the probabilities in terms of senses from a semantic hierarchy and exploit the fact that the senses can be grouped into classes consisting of semantically similar senses. There is a particular focus on the problem of how to determine a suitable class for a given sense, or, alternatively, how to determine a suitable level of generalization in the hierarchy. A procedure is developed that uses a chi-square test to determine a suitable level of generalization. In order to test the performance of the estimation method, a pseudo-disambiguation task is used, together with two alternative estimation methods. Each method uses a different generalization procedure; the first alternative uses the minimum description length principle, and the second uses Resnik's measure of selectional preference. In addition, the performance of our method is investigated using both the standard Pearson chi-square statistic and the log-likelihood chi-square statistic
On Horizontal and Vertical Separation in Hierarchical Text Classification
Hierarchy is a common and effective way of organizing data and representing
their relationships at different levels of abstraction. However, hierarchical
data dependencies cause difficulties in the estimation of "separable" models
that can distinguish between the entities in the hierarchy. Extracting
separable models of hierarchical entities requires us to take their relative
position into account and to consider the different types of dependencies in
the hierarchy. In this paper, we present an investigation of the effect of
separability in text-based entity classification and argue that in hierarchical
classification, a separation property should be established between entities
not only in the same layer, but also in different layers. Our main findings are
the followings. First, we analyse the importance of separability on the data
representation in the task of classification and based on that, we introduce a
"Strong Separation Principle" for optimizing expected effectiveness of
classifiers decision based on separation property. Second, we present
Hierarchical Significant Words Language Models (HSWLM) which capture all, and
only, the essential features of hierarchical entities according to their
relative position in the hierarchy resulting in horizontally and vertically
separable models. Third, we validate our claims on real-world data and
demonstrate that how HSWLM improves the accuracy of classification and how it
provides transferable models over time. Although discussions in this paper
focus on the classification problem, the models are applicable to any
information access tasks on data that has, or can be mapped to, a hierarchical
structure.Comment: Full paper (10 pages) accepted for publication in proceedings of ACM
SIGIR International Conference on the Theory of Information Retrieval
(ICTIR'16
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.Comment: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference
o
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality
Real-time occlusion handling is a major problem in outdoor mixed reality
system because it requires great computational cost mainly due to the
complexity of the scene. Using only segmentation, it is difficult to accurately
render a virtual object occluded by complex objects such as trees, bushes etc.
In this paper, we propose a novel occlusion handling method for real-time,
outdoor, and omni-directional mixed reality system using only the information
from a monocular image sequence. We first present a semantic segmentation
scheme for predicting the amount of visibility for different type of objects in
the scene. We also simultaneously calculate a foreground probability map using
depth estimation derived from optical flow. Finally, we combine the
segmentation result and the probability map to render the computer generated
object and the real scene using a visibility-based rendering method. Our
results show great improvement in handling occlusions compared to existing
blending based methods
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Hierarchical classification for multiple, distributed web databases
The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance
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