STFNet: Image Classification Model Based on Balanced Texture and Shape Features
Kyu-hong Hwang, Ho-rim Park, Young-Guk Ha · 2021
This paper proposes a network model that classifies objects in images by reflecting texture and shape in a balanced way. In the case of the existing image classification model, objects are classified depending on the texture of the object. Because of this characteristic, if noise occurs in the image or the texture is intentionally changed, the image classification model is likely to misclassify the object. In addition, it can be deliberately misclassified by noise generated by hostile attack, and if it is used in industrial sites closely related to humans, a big problem may occur. To solve these problems, this paper proposes a method of extracting texture and shape features from the classification image and blending them appropriately to classify objects in the image appropriately. The results of comparing and evaluating the performance with the existing model and a new network model proposed in this paper for a general image, texture converted image, and an image with hostile attack noise added are as follows: Similar performance was obtained in the general images, but the accuracy improved by 54.64% and 69.02% for the image with textured and hostile attack noised added when using the newly proposed network model.