Advanced content based image retrieval for fashion

In this paper we propose a new content based approach for clothing image retrieval trying to mimic the human vision understanding not only based on naive manipulation of texture and color, but also combining some recent and advanced techniques like human pose estimation, super-pixel segmentation and cloth parsing. Moreover, we exploit metric learning to improve the image matching phase by proposing a new approach to learn a distance properly designed for the analyzed application. Specially in fashion sector our work seems very helpful in obtaining more accurate categorization and naturally desirable image retrieval from a large database of images of models dressing various types of style, pattern and fashion. In particular, a drastic improvement is observed when the metric learning strategy is introduced.