  Content-based fashion image retrieval (CBFIR) has been widely used in our
daily life for searching fashion images or items from online platforms. In
e-commerce purchasing, the CBFIR system can retrieve fashion items or products
with the same or comparable features when a consumer uploads a reference image,
image with text, sketch or visual stream from their daily life. This lowers the
CBFIR system reliance on text and allows for a more accurate and direct
searching of the desired fashion product. Considering recent developments,
CBFIR still has limits when it comes to visual searching in the real world due
to the simultaneous availability of multiple fashion items, occlusion of
fashion products, and shape deformation. This paper focuses on CBFIR methods
with the guidance of images, images with text, sketches, and videos.
Accordingly, we categorized CBFIR methods into four main categories, i.e.,
image-guided CBFIR (with the addition of attributes and styles), image and
text-guided, sketch-guided, and video-guided CBFIR methods. The baseline
methodologies have been thoroughly analyzed, and the most recent developments
in CBFIR over the past six years (2017 to 2022) have been thoroughly examined.
Finally, key issues are highlighted for CBFIR with promising directions for
future research.
