The purpose of this paper is to use convolutional neural networks in order to solve the problem of the difficulty in the classification of cashmere and wool. To do the research, it proposes a low-dimensional strategy of using part-level features to enhance object-level features. The study aims to use computer version method to find out the most effective and robust method to manage the difficult task of cashmere and wool identification.
The authors try to get a coarse classification result and the initial weights of the model in the first step. The authors use the results of the first step and a Fast-RCNN method to extract part-level features in step 2. Finally, the authors mix the part-level features to enhance object-level features and classify the cashmere and wool images.
The paper finds that not only the texture is the key element of the cashmere and wool identification but also the image colors.
Most importantly, the paper finds that the part-level features can enhance object-level features in the fiber identification task. However, it does not work in contrast, and the strategy can be used in the similar fibers identifications.
