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

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

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

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.

本文言語英語
ホスト出版物のタイトル2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728139753
DOI
出版ステータス出版済み - 11月 2019
外部発表はい
イベント9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019 - Istanbul, トルコ
継続期間: 11月 6 201911月 9 2019

出版物シリーズ

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

会議

会議9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019
国/地域トルコ
CityIstanbul
Period11/6/1911/9/19

!!!All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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