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Learning models for semantic classification of insufficient plantar pressure images
Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and
effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set
learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose
an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are
introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset
of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by
using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)-
based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally,
the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained
CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition
methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H)
and time (training and evaluation). The proposed method for the plantar pressure classification task shows high
performance in most indices when comparing with other methods. The transfer learning-based method can be
applied to other insufficient data-sets of sensor imaging fields
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
Learning Finer-class Networks for Universal Representations
Many real-world visual recognition use-cases can not directly benefit from
state-of-the-art CNN-based approaches because of the lack of many annotated
data. The usual approach to deal with this is to transfer a representation
pre-learned on a large annotated source-task onto a target-task of interest.
This raises the question of how well the original representation is
"universal", that is to say directly adapted to many different target-tasks. To
improve such universality, the state-of-the-art consists in training networks
on a diversified source problem, that is modified either by adding generic or
specific categories to the initial set of categories. In this vein, we proposed
a method that exploits finer-classes than the most specific ones existing, for
which no annotation is available. We rely on unsupervised learning and a
bottom-up split and merge strategy. We show that our method learns more
universal representations than state-of-the-art, leading to significantly
better results on 10 target-tasks from multiple domains, using several network
architectures, either alone or combined with networks learned at a coarser
semantic level.Comment: British Machine Vision Conference (BMVC) 201
Learning Models for Semantic Classification of Insufficient Plantar Pressure Images
Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields
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