TY - GEN
T1 - A Genetic Programming Approach to Integrate Multilayer CNN Features for Image Classification
AU - Chu, Wei Ta
AU - Chu, Hao An
N1 - Funding Information:
This work was partially supported by the Ministry of Science and Technology under the grant 107-2221-E-194-038-MY2 and 107-2218-E-002-054, and the Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
Funding Information:
Acknowledgement. This work was partially supported by the Ministry of Science and Technology under the grant 107-2221-E-194-038-MY2 and 107-2218-E-002-054, and the Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-030-05710-7_53
DO - 10.1007/978-3-030-05710-7_53
M3 - Conference contribution
AN - SCOPUS:85059856369
SN - 9783030057091
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 640
EP - 651
BT - MultiMedia Modeling - 25th International Conference, MMM 2019, Proceedings
A2 - Kompatsiaris, Ioannis
A2 - Vrochidis, Stefanos
A2 - Mezaris, Vasileios
A2 - Cheng, Wen-Huang
A2 - Huet, Benoit
A2 - Gurrin, Cathal
PB - Springer Verlag
T2 - 25th International Conference on MultiMedia Modeling, MMM 2019
Y2 - 8 January 2019 through 11 January 2019
ER -