9,604 research outputs found
Compositional Model based Fisher Vector Coding for Image Classification
Deriving from the gradient vector of a generative model of local features,
Fisher vector coding (FVC) has been identified as an effective coding method
for image classification. Most, if not all, FVC implementations employ the
Gaussian mixture model (GMM) to depict the generation process of local
features. However, the representative power of the GMM could be limited because
it essentially assumes that local features can be characterized by a fixed
number of feature prototypes and the number of prototypes is usually small in
FVC. To handle this limitation, in this paper we break the convention which
assumes that a local feature is drawn from one of few Gaussian distributions.
Instead, we adopt a compositional mechanism which assumes that a local feature
is drawn from a Gaussian distribution whose mean vector is composed as the
linear combination of multiple key components and the combination weight is a
latent random variable. In this way, we can greatly enhance the representative
power of the generative model of FVC. To implement our idea, we designed two
particular generative models with such a compositional mechanism.Comment: Fixed typos. 16 pages. Appearing in IEEE T. Pattern Analysis and
Machine Intelligence (TPAMI
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods.Comment: 8 pages, accepted by AAAI201
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