Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Local descriptor coding is one crucial step in traditional Bag #R##N# of Words (BoW) framework for image categorization. Hover, the slow #R##N# coding speed of previous methods is one limitation for applications in #R##N# large scale problems. Recently, neural network based models have been #R##N# widely applied in various classification tasks. Using neural network models for descriptor coding is straightforward and efficient due to their fast #R##N# forward propagation. ,  propose to use the Auto-Encoder #R##N# (AE) network as a local descriptor coding block, and further embed AE #R##N# network in the BoW framework for the purpose of image classification. #R##N# To make the hidden activities of AE network to be both selective and #R##N# sparse,  add an efficient and effective regularization term into the learning process of AE network, which can promote sparsity of the hidden #R##N# layer for each input descriptor as ll as the selectivity for each hidden #R##N# node. By incorporating the AE network coding with the BoW framework, #R##N#  can achieve better results and faster speeds than other state-of-theart feature coding methods on Caltech101, Scene15 and UIUC 8-Sports #R##N# databases.
['Guo-Sen Xie', 'Xu-Yao Zhang', 'Cheng-Lin Liu']
I am writing a research paper on a idea Efficient Feature Coding Based on Auto-encoder Network for Image Classification
[{"from": "human", "value": "I am writing a research paper on a idea Efficient Feature Coding Based on Auto-encoder Network for Image Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Local descriptor coding is one crucial step in traditional Bag #R##N# of Words (BoW) framework for image categorization. Hover, the slow #R##N# coding speed of previous methods is one limitation for applications in #R##N# large scale problems. Recently, neural network based models have been #R##N# widely applied in various classification tasks. Using neural network models for descriptor coding is straightforward and efficient due to their fast #R##N# forward propagation. ,  propose to use the Auto-Encoder #R##N# (AE) network as a local descriptor coding block, and further embed AE #R##N# network in the BoW framework for the purpose of image classification. #R##N# To make the hidden activities of AE network to be both selective and #R##N# sparse,  add an efficient and effective regularization term into the learning process of AE network, which can promote sparsity of the hidden #R##N# layer for each input descriptor as ll as the selectivity for each hidden #R##N# node. By incorporating the AE network coding with the BoW framework, #R##N#  can achieve better results and faster speeds than other state-of-theart feature coding methods on Caltech101, Scene15 and UIUC 8-Sports #R##N# databases."}]