RGB Channel Combinations Method for Feature Extraction in Image Analysis

Seo-El Lee, Seong-Un Cho, Kyungyong Chung, Hyun Yoo


Latest image analysis deep learning algorithms use diverse methods to extract features from images based on the Convolution Neural Network (CNN). CNN has a convolution layer consisting of RGB as three overlapping channels in the feature extraction process, and such architecture enables the backbone network to flow without losing each hue information. Therefore, 3D input data consisting of 3 channels to process the RGB channel consists of a large-scale neural network with many layer blocks. This processing method exhibits high accuracy. However, in terms of practicality, it results in big inefficiencies such as memory overhead and computational overhead. This study proposes the RGB Channel Combinations Method for Feature Extraction in Image Analysis to resolve such inefficiencies. This is a method that converts the RGB value into one tensor structure by combining each weight and bias and makes it possible to pass through the backbone network without damaging hue information. Based on the experiment results, it is confirmed that the accuracy decreased by 1.42% compared to the pre-existing method, but the number of parameters used by the input layer decreased. It is confirmed that the pre-processing used in the proposed method gained an additional computational overhead, but the number of input parameters decreased to 1/3 in the feature extraction stage performed afterward. As the proposed method applies to all image analysis algorithms, its expandability is extremely high and can process a large amount of image data.


Convolution neural network; rgb channel; feature extraction; video analysis; computer vision

Full Text:



Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, "A survey of convolutional neural networks: analysis, applications, and prospects," IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 12, pp. 6999-7019, Dec. 2022.

Z. Q. Zhao, P. Zheng, S. T. Xu, and X. Wu, "Object detection with deep learning: A review," IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 11, pp. 3212-3232, Nov. 2019.

S. Peng, S. Sun, and Y. D. Yao, "A survey of modulation classification using deep learning: Signal representation and data preprocessing," IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 12, pp. 7020-7038, Dec. 2021.

L. Duan, J. Liu, W. Yang, T. Huang, and W. Gao, "Video coding for machines: A paradigm of collaborative compression and intelligent analytics," Transactions on Image Processing, vol. 29, pp. 8680-8695, 2020.

Li, Lianlin, and Tie Jun Cui, "Information metamaterials–from effective media to real-time information processing systems." Nanophotonics, vol. 8, no. 5, pp. 703-724, Feb. 2019.

S. Zhang, W. Huang, and C. Zhang, "Three-channel convolutional neural networks for vegetable leaf disease recognition," Cognitive Systems Research, vol. 53, pp. 31-41, 2019.

N. Dryden, N. Maruyama, T. Moon, T. Benson, M. Snir, and B. Van Essen, "Channel and filter parallelism for large-scale CNN training," in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-20, Nov. 2019.

F. Lingling, Z. Hanyu, Z. Jiaxin, and W. Xianghai, “Image classification with an RGB-channel nonsubsampled contourlet transform and a convolutional neural network,†Neurocomputing, 396, pp. 266-277, 2020.

M. Masana, X. Liu, B. Twardowski, M. Menta, A. D. Bagdanov, and J. van de Weijer, "Class-incremental learning: survey and performance evaluation on image classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, Oct. 2022.

S. Jiang et al., “An Optimized Deep Neural Network Detecting Small and Narrow Rectangular Objects in Google Earth Images,†in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 1068-1081, 2020.

Y. Ming, X. Meng, C. Fan, and H. Yu, "Deep learning for monocular depth estimation: A review," Neurocomputing, vol. 438, pp. 14-33, May 2021.

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, "Deep learning for computer vision: A brief review," Computational Intelligence and Neuroscience, 2018.

C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.

Z. Cheng, Q. Yang, and B. Sheng, "Colorization using neural network ensemble," IEEE Transactions on Image Processing, vol. 26, no. 11, pp. 5491-5505, Nov. 2017.

Z. Lu, X. Jiang, and A. C. Kot, “A Color Channel Fusion Approach for Face Recognition,†IEEE Signal Processing Letters, vol. 22, no. 11, pp. 1839-1843, 2015.

L. Zhao et al., “Color Channel Fusion Network for Low-Light Image Enhancement,†IEEE International Conference on Image Processing (ICIP), pp. 1654-1658, 2021.

Y. J. Kim et al., “RGB Channel Superposition Algorithm with Acetowhite Mask Images in a Cervical Cancer Classification Deep Learning Model,†Sensors, vol. 22, no. 9, 3564, 2022.

M. Mittal et al., "An efficient edge detection approach to provide better edge connectivity for image analysis." IEEE Access, vol. 7, pp. 33240-33255, 2019.

D. Singh, K. Vijay, and K. Manjit, "Densely connected convolutional networks-based COVID-19 screening model." Applied Intelligence, vol. 51, pp. 3044-3051, 2021.

Y. Kim, C. Jang, J. Demouth, and S. Lee, “Robust color-to-gray via nonlinear global mapping,†in ACM SIGGRAPH Asia 2009 papers, pp. 1-4, 2009.

T. Kumar and K. Verma, “A Theory Based on Conversion of RGB image to Gray image,†International Journal of Computer Applications, vol. 7, no. 2, pp. 7-10, 2010.

K. Kumar, R. K. Mishra, and D. Nandan, “Efficient Hardware of RGB to Gray Conversion Realized on FPGA and ASIC,†Procedia Computer Science, vol. 171, pp. 2008-2015, 2020.

J. Hu, Q. Jiang, R. Cong, W. Gao, and F. Shao, "Two-branch deep neural network for underwater image enhancement in HSV color space," IEEE Signal Processing Letters, vol. 28, pp. 2152-2156, 2021.

J. F. Yang, K. T. Lee, G. C. Chen, W. J. Yang, and L. Yu, "A YCbCr color depth packing method and its extension for 3D video broadcasting services," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 9, pp. 3043-3053, 2019.

P. Fan, G. Lang, B. Yan, X. Lei, P. Guo, Z. Liu, and F. Yang, "A method of segmenting apples based on gray-centered RGB color space," Remote Sensing, vol. 13, no. 6, pp. 1211, 2021.

N. Dubey, H. Modi, "A robust discrete wavelet transform based adaptive watermarking scheme in YCbCr color space against camcorder recording in cinema/movie theatres," Engineered Science, vol. 15, pp. 116-128, 2021.

N. Danapur, S. A. A. Dizaj, and V. Rostami, "An efficient image retrieval based on an integration of HSV, RLBP, and CENTRIST features using ensemble classifier learning," Multimedia Tools and Applications, vol. 79, no. 33-34, pp. 24463-24486, 2020.

A. Garg, X. W. Pan, L. R. Dung, "LiCENt: Low-light image enhancement using the light channel of HSL," IEEE Access, vol. 10, pp. 33547-33560, 2022.

Krizhevsky, Alex, and Geoffrey Hinton. "Learning multiple layers of features from tiny images." (2009): 7.

A. Luque, A. Carrasco, A. Martín, and A. Heras, "The impact of class imbalance in classification performance metrics based on the binary confusion matrix," Pattern Recognition, vol. 91, pp. 216-231, 2019.

DOI: http://dx.doi.org/10.18517/ijaseit.13.3.18388


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development