Abstract
In the field of image processing, all types of computation models are almost evolved to solve the issues through encoded neurons. However, compared with decoding orientation and regression analysis, still the doors are open due to its complexity. At present technologies uses two steps such as, decoding the intermediate terms and reconstruction using decoded information. The performance in terms of regression analysis is lagging due to the decoded intermediate terms. Conventional neural network models perform better in feature classification and representation, though the performance is reduced while handling high level features. Considering these issues in image classification and regression, the proposed model is designed with capsule network as an innovative method which is suitable to handle high level features. The experimental results of the proposed model are compared with conventional neural network models such as BPNN and CNN to validate the superior performance. The proposed model achieves better retrieval efficiency of 95.4% which is much better than other neural network models.
References
Bashar, A. (2019). Survey on Evolving Deep Learning Neural Network Architectures. Journal of Artificial Intelligence, 1(02), 73-82.
Vijayakumar, T. (2019). Comparative Study of Capsule Neural Network in Various Applications. Journal of Artificial Intelligence, 1(01), 19-27.
Shaohui Lin, Ling Cai, Xianming Lin, Rongrong Ji (2016). Masked face detection via a modified LeNet. Neurocomputing. 218, 197-202
Mostafa Elsaadouny, Jan Barowski, Ilona Rolfes (2020). Extracting the Features of the Shallowly Buried Objects using LeNet Convolutional Network. European Conference on Antennas and Propagation,1-4
Rui Wang, Jingwen Xu, Tony X. Han (2019). Object instance detection with pruned Alexnet and extended training data. Signal Processing: Image Communication, 70, 145-156
Anju Unnikrishnan, Sowmya V, Soman K P (2018). Deep AlexNet with Reduced Number of Trainable Parameters for Satellite Image Classification. Procedia Computer Science, 143, 931-938
Siyuan Lu, Zhihai Lu, Yu-Dong Zhang (2019). Pathological brain detection based on AlexNet and transfer learning. Journal of Computational Science. 30,41-47
Zhenyu Lu, Yanzhong Bai, Yi Chen, Chunqiu Su, Shuihua Wang (2020). The classification of gliomas based on a Pyramid dilated convolution ResNet model. Pattern Recognition Letters. 133, 173-179
Shuai Peng, Hongbo Huang, Weijun Chen, Liang Zhang, Weiwei Fang (2020). More trainable inception-ResNet for face recognition. Neurocomputing. 411,9-19
Shaopeng Liu, Guohui Tian, Yuan Xu (2019). A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter. Neurocomputing. 338, 191-206