599 research outputs found
A human motion feature based on semi-supervised learning of GMM
Using motion capture to create naturally looking motion sequences for virtual character animation has become a standard procedure in the games and visual effects industry. With the fast growth of motion data, the task of automatically annotating new motions is gaining an importance. In this paper, we present a novel statistic feature to represent each motion according to the pre-labeled categories of key-poses. A probabilistic model is trained with semi-supervised learning of the Gaussian mixture model (GMM). Each pose in a given motion could then be described by a feature vector of a series of probabilities by GMM. A motion feature descriptor is proposed based on the statistics of all pose features. The experimental results and comparison with existing work show that our method performs more accurately and efficiently in motion retrieval and annotation
A Study of Boosting based Transfer Learning for Activity and Gesture Recognition
abstract: Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classify unseen instances occurring in a different, but related "target" domain. The algorithm is evaluated on real-world classification problems namely accelerometer based 3D gesture recognition, smart home activity recognition and text categorization. The performance on these datasets is analyzed and evaluated against popular boosting-based instance transfer techniques. In addition, supporting empirical studies, that investigate some of the less explored bottlenecks of boosting based instance transfer methods, are presented, to understand the suitability and effectiveness of this form of knowledge transfer.Dissertation/ThesisM.S. Computer Science 201
Multi-Instance Multi-Label Learning
In this paper, we propose the MIML (Multi-Instance Multi-Label learning)
framework where an example is described by multiple instances and associated
with multiple class labels. Compared to traditional learning frameworks, the
MIML framework is more convenient and natural for representing complicated
objects which have multiple semantic meanings. To learn from MIML examples, we
propose the MimlBoost and MimlSvm algorithms based on a simple degeneration
strategy, and experiments show that solving problems involving complicated
objects with multiple semantic meanings in the MIML framework can lead to good
performance. Considering that the degeneration process may lose information, we
propose the D-MimlSvm algorithm which tackles MIML problems directly in a
regularization framework. Moreover, we show that even when we do not have
access to the real objects and thus cannot capture more information from real
objects by using the MIML representation, MIML is still useful. We propose the
InsDif and SubCod algorithms. InsDif works by transforming single-instances
into the MIML representation for learning, while SubCod works by transforming
single-label examples into the MIML representation for learning. Experiments
show that in some tasks they are able to achieve better performance than
learning the single-instances or single-label examples directly.Comment: 64 pages, 10 figures; Artificial Intelligence, 201
- …