2018 24th International Conference on Pattern Recognition (ICPR)
会议日期
2018
会议地点
Beijing, China
摘要
It is meaningful to decode the semantic information
from functional magnetic resonance imaging (fMRI) brain signals
evoked by natural images. Semantic decoding can be viewed
as a classification problem. Since a natural image may contain
many semantic information of different objects, the single label
classification model is not appropriate to cope with semantic
decoding problem, which motivates the multi-label classification
model. However, most multi-label models always treat each label
equally. Actually, if dataset is associated with a large number of
semantic labels, it will be difficult to get an accurate prediction
of semantic label when the label appears with a low frequency
in this dataset. So we should increase the relative importance
degree to the labels that associate with little instances. In order
to improve multi-label prediction performance, in this paper, we
firstly propose a multinomial label distribution to estimate the
importance degree of each associated label for an instance by
using conditional probability, and then establish a deep neural
network (DNN) based model which contains both multinomial
label distribution and label co-occurrence information to realize
the multi-label classification of semantic information in fMRI
brain signals. Experiments on three fMRI recording datasets
demonstrate that our approach performs better than the stateof-the-art methods on semantic information prediction.
1.Research Center for Brain-inspired Intelligenceand National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences, Beijing, China 2.University of Chinese Academy of Sciences, Beijing, China 3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
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