CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Efficient Feature Coding Based on Auto-encoder Network for Image Classification
Xie, Guo-Sen; Zhang, Xu-Yao; Liu, Cheng-Lin
2014
Conference NameAsian Conference on Computer Vision
Source PublicationProcceding of ACCV 2014
Conference Date2014-11
Conference Place新加坡
AbstractLocal descriptor coding is one crucial step in traditional Bag
of Words (BoW) framework for image categorization. However, the slow
coding speed of previous methods is one limitation for applications in
large scale problems. Recently, neural network based models have been
widely applied in various classification tasks. Using neural network models for descriptor coding is straightforward and efficient due to their fast
forward propagation. In this paper, we propose to use the Auto-Encoder
(AE) network as a local descriptor coding block, and further embed AE
network in the BoW framework for the purpose of image classification.
To make the hidden activities of AE network to be both selective and
sparse, we add an efficient and effective regularization term into the learning process of AE network, which can promote sparsity of the hidden
layer for each input descriptor as well as the selectivity for each hidden
node. By incorporating the AE network coding with the BoW framework,
we can achieve better results and faster speeds than other state-of-theart feature coding methods on Caltech101, Scene15 and UIUC 8-Sports
databases.

KeywordFeature Coding
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11957
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorXie, Guo-Sen
Recommended Citation
GB/T 7714
Xie, Guo-Sen,Zhang, Xu-Yao,Liu, Cheng-Lin. Efficient Feature Coding Based on Auto-encoder Network for Image Classification[C],2014.
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