Parsing Clothes in Unrestricted Images

By Nataraj Jammalamadaka, Ayush Minocha, Digvijay Singh, C. V. Jawahar. 2013

18 citations recorded by the source corpus.

Parsing for clothes in images and videos is a critical step towards understanding the human appearance. In this work, we propose a method to segment clothes in settings where there is no restriction on number and type of clothes, pose of the person, viewing angle, occlusion and number of people. This is a challenging task as clothes, even of the same category, have large variations in color and texture. The presence of human joints is the best indicator for cloth types as most of the clothes are consistently worn around the joints. We incorporate the human joint prior by estimating the body joint distributions using the detectors and learning the cloth-joint co-occurrences of different cloth types with respect to body joints. The cloth-joint and cloth-cloth co-occurrences are used as a part of the conditional random field framework to segment the image into different clothing. Our results indicate that we have outperformed the recent attempt [16] on H3D [3], a fairly complex dataset.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

A High Performance CRF Model for Clothes Parsing (2014, background): “This is reflected in the growing interest in recognizing clothing from images [3,4,5,6,7,8,9,10], as this can enable a wide variety of applications such as trying on virtual garments in online shopping.”