Generic and specific impression estimation of clothing fabric images based on machine learning

Consumers´ psychological feeling or impression is an important factor for product design. The impression estimation becomes an important issue. In this paper, we propose generic and specific impression estimation methods based on machine learning for cloth fabric images. We use a semantic differential (SD) method to measure the user´s impression such as bright, warm while they viewing a cloth fabric image. We also extract both global and local features of cloth fabric images such as color and texture using computer vision techniques. Then we use support vector regression to model the mapping functions between the generic impression (or specific impression) and image features. The learned mapping functions are used to estimate the generic or specific impression of cloth fabric images.