TY - GEN
T1 - Leveraging the Potency of CNN for Efficient Assessment of Visual Complexity of Images
AU - Abdelwahab, Mohamed A.
AU - Iliyasu, Abdullah M.
AU - Salama, Ahmed S.
N1 - Funding Information:
This study is sponsored by the Prince Sattam Bin Abdulaziz University, Saudi Arabia via the Deanship for Scientific Research funding for the Advanced Computational Intelligence & Intelligent Systems (ACIIS) Research Group Project Number 2019/01/9862
Funding Information:
This study is sponsored by the Prince Sattam Bin Abdulaziz University, Saudi Arabia via the Deanship for Scientific Research funding for the Advanced Computational Intelligence and Intelligent Systems (ACIIS) Research Group Project Number 2019/01/9862
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
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U2 - 10.1109/IPTA.2019.8936095
DO - 10.1109/IPTA.2019.8936095
M3 - Conference contribution
AN - SCOPUS:85077959051
T3 - 2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019
BT - 2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019
Y2 - 6 November 2019 through 9 November 2019
ER -