Leveraging the Potency of CNN for Efficient Assessment of Visual Complexity of Images

Mohamed A. Abdelwahab, Abdullah M. Iliyasu, Ahmed S. Salama

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

Abstract

Achieving human-level interpretation of visual complexity will have numerous applications in data hiding, image compression, image retrieval, computer vision, etc. Previous studies relied on using unsupervised learning to coalesce handcrafted image features, such as edges and colours, for assessment of visual complexity. Our study utlises the potency of Convolutional Neural Networks (CNNs) to improve the classification accuracy and assessment of visual complexity based on the Corel 1000A dataset. We incorporated SVM-based supervised learning to classify the features extracted by the CNN. Furthermore, we exploited the utility offered by fine tuning and appropriate adjustments to the CNN structure that were incorporated into our learning strategy which led to 13.6 % improvement in classification accuracy than the available (unsupervised) and supervised learning methods.

Original languageEnglish
Title of host publication2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728139753
DOIs
Publication statusPublished - Nov 2019
Externally publishedYes
Event9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019 - Istanbul, Turkey
Duration: Nov 6 2019Nov 9 2019

Publication series

Name2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019

Conference

Conference9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019
CountryTurkey
CityIstanbul
Period11/6/1911/9/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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