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

Wei Ta Chu, Hao An Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 25th International Conference, MMM 2019, Proceedings
EditorsIoannis Kompatsiaris, Stefanos Vrochidis, Vasileios Mezaris, Wen-Huang Cheng, Benoit Huet, Cathal Gurrin
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030057091
Publication statusPublished - 2019
Event25th International Conference on MultiMedia Modeling, MMM 2019 - Thessaloniki, Greece
Duration: 2019 Jan 82019 Jan 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11295 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on MultiMedia Modeling, MMM 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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