Parsing Clothes in Unrestricted Images

Cloth parsing involves locating and describing all the clothes (e.g., T-shirt, shorts) and accessories (e.g, bag) that the person is wearing. The main challenges in solving this include the large variety of clothing patterns that have been developed across the globe by different cultures. Occlusions from other humans or objects, viewing angle and heavy clutter in the background further complicates the problem. In the recent past, Yamaguchi et al. [4] have proposed a method to parse clothes for fashion photographs where the image settings are simple with no clutter or occlusion. In our work, we aim to segment clothes in unconstrained settings by modelling the cloth to its body part vicinity in a CRF framework. Poselets [2] are adapted to obtain body part locations, as alternatives like human pose estimation algorithms frequently fail and give wrong pose estimates under occlusions and clutter. Given an image, first the superpixels and body joint locations are computed. These superpixels form the vertices V of the CRF. Two superpixels which share a border are considered adjacent and are connected by an edge e ∈ E. The best labeling using the CRF model is given by the equation, L̂ = argmaxLP(L|Z, I), (1)