  Several applications require demographic information of ordinary people in
unconstrained scenarios. This is not a trivial task due to significant human
appearance variations. In this work, we introduce trixels for clustering image
regions, enumerating their advantages compared to superpixels. The classical
GrabCut algorithm is later modified to segment trixels instead of pixels in an
unsupervised context. Combining with face detection lead us to a clothes
segmentation approach close to real time. The study uses the challenging Pascal
VOC dataset for segmentation evaluation experiments. A final experiment
analyzes the fusion of clothes features with state-of-the-art gender
classifiers in ClothesDB, revealing a significant performance improvement in
gender classification.
