A Genetic Programming Approach to Integrate Multilayer CNN Features for Image Classification

Wei Ta Chu, Hao An Chu

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

Fusing information extracted from multiple layers of a convolutional neural network has been proven effective in several domains. Common fusion techniques include feature concatenation and Fisher embedding. In this work, we propose to fuse multilayer information by genetic programming (GP). With the evolutionary strategy, we iteratively fuse multilayer information in a systematic manner. In the evaluation, we verify the effectiveness of discovered GP-based representations on three image classification datasets, and discuss characteristics of the GP process. This study is one of the few works to fuse multilayer information based on an evolutionary strategy. The reported preliminary results not only demonstrate the potential of the GP fusion scheme, but also inspire future study in several aspects.

原文English
主出版物標題MultiMedia Modeling - 25th International Conference, MMM 2019, Proceedings
編輯Ioannis Kompatsiaris, Stefanos Vrochidis, Vasileios Mezaris, Wen-Huang Cheng, Benoit Huet, Cathal Gurrin
發行者Springer Verlag
頁面640-651
頁數12
ISBN(列印)9783030057091
DOIs
出版狀態Published - 2019
事件25th International Conference on MultiMedia Modeling, MMM 2019 - Thessaloniki, Greece
持續時間: 2019 一月 82019 一月 11

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11295 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference25th International Conference on MultiMedia Modeling, MMM 2019
國家/地區Greece
城市Thessaloniki
期間19-01-0819-01-11

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)

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