Photo Filter Classification and Filter Recommendation without Much Manual Labeling

Wei Ta Chu, Yu Tzu Fan

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

Abstract

Because how users employ filters to photos may reveal user's preference or mental state, a photo filter classification method is potentially demanded to enable future large-scale analysis. We adopt the transfer learning technique to transform deep models pre-trained for object classification into models suitable for photo filter classification. Based on accurate classification results, we build a filter recommendation approach without much manual labeling. It can be easily extended when more training data are available. A series of experimental studies are conducted to demonstrate effectiveness of filter classification with transfer learning. We also demonstrate the proposed filter recommendation achieves encouraging performance.

Original languageEnglish
Title of host publicationIEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728118178
DOIs
Publication statusPublished - 2019 Sep
Event21st IEEE International Workshop on Multimedia Signal Processing, MMSP 2019 - Kuala Lumpur, Malaysia
Duration: 2019 Sep 272019 Sep 29

Publication series

NameIEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019

Conference

Conference21st IEEE International Workshop on Multimedia Signal Processing, MMSP 2019
CountryMalaysia
CityKuala Lumpur
Period19-09-2719-09-29

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology

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  • Cite this

    Chu, W. T., & Fan, Y. T. (2019). Photo Filter Classification and Filter Recommendation without Much Manual Labeling. In IEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019 [8901831] (IEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MMSP.2019.8901831