i-manager Publications

Implementation of Image Analysis System by Using Convolution Neural Networks

B. Sridhar*, Ch. Sowjanya**, Chinta Vamsi Sai Krishna***, D. Govind Rao****

*-**** Department of Electronics and Communication Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India.

Periodicity:October - December'2019
DOI : https://doi.org/10.26634/jip.6.4.16813

Abstract

In deep learning neural network architectures, the term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150. One of the most popular types of deep learning neural networks is known as Convolution Neural Networks (CNN). Image Analysis is the process to analyse various feature of the image such as segmentation of the image, classification of the images, etc. In this proposed work, a CNN based Image Analysis System has been proposed to detect the objects in real time image and classify the image based on the feature of extraction. The proposed method is implemented by using MATLAB and Python software. The system is trained with the CIFAR -10 and MINST Dataset and own image database. The performance of the proposed method is evaluated by using accuracy and loss factor. The proposed system is tested with unknown samples of images and also trained for accuracy and average test loss.

Keywords

Image Analysis, Deep Learning, Convolution Neural Networks.

How to Cite this Article?

Sridhar, B., Sowjanya, Ch., Krishna, C. V. S., and Rao, D. G. (2019). Implementation of Image Analysis System by Using Convolution Neural Networks. i-manager's Journal on Image Processing, 6(4), 28-38. https://doi.org/10.26634/jip.6.4.16813

References

[1]. Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde- Farley, D., & Bengio, Y. (2012). Theano: New features and speed improvements. Deep Learning Workshop, NIPS 2012 (pp. 1-10).

[2]. Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., & Bengio, Y. (2010, June). Theano: A CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy), 4(3), 1-7.

[3]. Chen, H., Qi, X. J., Cheng, J. Z., & Heng, P. A. (2016, February). Deep contextual networks for neuronal structure segmentation. In Thirtieth AAAI Conference on Artificial Intelligence (pp. 1167-1173).

[4]. Cuda-Convnet (n. d.). Google Code Archive. Retrieved from https://code.google.com/p/cudaconvnet/

[5]. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248-255). IEEE. https://doi.org/10.1109/CVPR.2009.5206848 Source: DBLP

[6]. Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2015). The Pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1), 98-136. https://doi. org/10.1007/s11263-014-0733-5

[7]. Foulds, J., & Frank, E. (2010). A review of multiinstance learning assumptions. The Knowledge Engineering Review, 25(1), 1-25. https://doi.org/10.1017/ S026988890999035X

[8]. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 580-587). https://doi.org/10.1109/cvpr.2014.81

[9]. Hatipoglu, N., & Bilgin, G. (2014, October). Classification of histopathological images using convolutional neural network. In 2014 4^th International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). IEEE. https://doi.org/10.1109/ IPTA.2014.7001976

[10]. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1026-1034). https://doi.org/10.1109/ICCV.2015.123

[11]. Jain, V., Murray, J. F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K. L., Helmstaedter, M. N., Denk, W., & Seung, H. S. (2007, October). Supervised learning of image restoration with convolutional networks. In 2007 IEEE 11^th International Conference on Computer Vision (pp. 1-8). IEEE. https://doi.org/10.1109/ICCV.2007.4408909

[12]. Kraus, O. Z., Jimmy, L., & Frey, B. (2015). Classification and segmenting microscopy images using convolutional multiple instance learning. Bioinformatics, 32 (12), i52-i59. https://doi.org/10.1093/bioinformatics/ btw252

[13]. Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images (Vol. 1, No. 4, p. 7). Technical Report, University of Toronto.

[14]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25(2), 1097-1105. http://doi.org/10.1145/ 3065386

[15]. Ljosa, V., Sokolnicki, K. L., & Carpenter, A. E. (2012). Annotated high-throughput microscopy image sets for validation. Nature Methods, 9(7), 637-637. http://doi.org/ 10.1038/nmeth.2083

[16]. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3431-3440). https://doi.org/ 10.1109/CVPR.2015.7298965

[17]. Peng, H. (2008). Bioimage informatics: A new area of engineering biology. Bioinformatics, 24(17), 1827-1836. https://doi.org/10.1093/bioinformatics/btn346

[18]. Pinherio, R. C. P. H., & Pedro, H. (2014). Recurrent convolutional neural networks for scene parsing. In Proceedings of the 31^st on International Conference on Machine Learning (ICML-14), 32, I-82-I-90.

[19]. Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp.234-241). Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28

[20]. Socher, R., Lin, C. C., Manning, C., & Ng, A. Y. (2011). Parsing natural scenes and natural language with recursive neural networks. In Proceedings of the 28^th International Conference on Machine Learning (ICML- 11) (pp. 129-136).

[21]. Wählby, C., Kamentsky, L., Liu, Z. H., Riklin-Raviv, T., Conery, A. L., O'rourke, E. J., Sokolnicki, K. L., Visvikis, O., Ljosa, V., Irazoqui, J. E., Golland, P., Ruvkun, G., Ausubel, F. M., & Carpenter, A. E. (2012). An image analysis toolbox for high-throughput C. elegans assays. Nature Methods, 9(7), 714-716. https://doi.org/10.1038/nmeth.1984

[22]. Wiehman, S., & de villiars, H. (2016). Semantic segmentation of bio images using Convolution Neural Networks. 2016 International Joint Conference on Neural Networks (IJCNN), (pp. 624-631). https://doi.org/10.1109/ IJCNN.2016.7727258

[23]. Zeiler, M. D. (2012). ADADELTA: An adaptive learning rate method. arXiv preprint arXiv:1212.5701.

Implementation of Image Analysis System by Using Convolution Neural Networks

Abstract

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