Abustruct
In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six diffierent Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the e®ectiveness of the proposed method using Fashion-MNIST dataset and 70 000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classi¯cation accuracy. VGG-IE approach has the highest classification accuracy.
Links
This paper can be found in Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm
Reference
Liu, J., Zheng, Y., Dong, K., Yu, H., Zhou, J., Jiang, Y., Jiang, Z., Guo, S. and Ding, R., 2020. Classification of fashion article images based on improved random forest and VGG-IE algorithm. International Journal of Pattern Recognition and Artificial Intelligence, 34(03), p.2051004.