@conference {Zheng2014,
	title = {Dense semantic image segmentation with objects and attributes},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2014},
	pages = {3214{\textendash}3221},
	abstract = {The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. {\textquoteright}I see a shiny red chair{\textquoteright}). In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU indoor scenes datasets show that the proposed approach is able to achieve state-of-the-art results.},
	keywords = {Attributes, Image segmentation, Object recognition, Scene Understanding},
	isbn = {9781479951178},
	issn = {10636919},
	doi = {10.1109/CVPR.2014.411},
	url = {http://www.robots.ox.ac.uk/\~{}tvg/http://tu-dresden.de/inf/cvld},
	author = {Zheng, Shuai and Cheng, Ming Ming and Warrell, Jonathan and Sturgess, Paul and Vineet, Vibhav and Carsten Rother and Torr, Philip H.S.}
}
@article {Shotton2009,
	title = {TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context},
	journal = {International Journal of Computer Vision},
	volume = {81},
	number = {1},
	year = {2009},
	pages = {2{\textendash}23},
	abstract = {This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695-702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.). {\textcopyright} 2007 Springer Science+Business Media, LLC.},
	keywords = {Boosting, Conditional random field, Context, image understanding, Layout, Object recognition, Piecewise training, Segmentation, Semantic image segmentation, Textons, Texture},
	issn = {09205691},
	doi = {10.1007/s11263-007-0109-1},
	url = {http://jamie.shotton.org/work/code.html},
	author = {Shotton, Jamie and Winn, John and Carsten Rother and Criminisi, Antonio}
}
