@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}
}
@conference {Hoiem2007,
	title = {3D LayoutCRF for multi-view object class recognition and segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2007},
	abstract = {We introduce an approach to accurately detect and segment partially occluded objects in various viewpoints and scales. Our main contribution is a novel framework for combining object-level descriptions (such as position, shape, and color) with pixel-level appearance, boundary, and occlusion reasoning. In training, we exploit a rough 3D object model to learn physically localized part appearances. To find and segment objects in an image, we generate proposals based on the appearance and layout of local parts. The proposals are then refined after incorporating object-level information, and overlapping objects compete for pixels to produce a final description and segmentation of objects in the scene. A further contribution is a novel instance penalty, which is handled very efficiently during inference. We experimentally validate our approach on the challenging PASCAL{\textquoteright}06 car database. {\textcopyright} 2007 IEEE.},
	isbn = {1424411807},
	issn = {10636919},
	doi = {10.1109/CVPR.2007.383045},
	author = {Hoiem, Derek and Carsten Rother and Winn, John}
}
@conference {Kannan2007a,
	title = {Clustering appearance and shape by learning jigsaws},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2007},
	pages = {657{\textendash}664},
	abstract = {Patch-based appearance models are used in a wide range of computer vision applications. To learn such models it has previously been necessary to specify a suitable set of patch sizes and shapes by hand. In the jigsaw model presented here, the shape, size and appearance of patches are learned automatically from the repeated structures in a set of training images. By learning such irregularly shaped {\textquoteright}jigsaw pieces{\textquoteright}, we are able to discover both the shape and the appearance of object parts without supervision. When applied to face images, for example, the learned jigsaw pieces are surprisingly strongly associated with face parts of different shapes and scales such as eyes, noses, eyebrows and cheeks, to name a few. We conclude that learning the shape of the patch not only improves the accuracy of appearance-based part detection but also allows for shape-based part detection. This enables parts of similar appearance but different shapes to be distinguished; for example, while foreheads and cheeks are both skin colored, they have markedly different shapes.},
	isbn = {9780262195683},
	issn = {10495258},
	doi = {10.7551/mitpress/7503.003.0087},
	author = {Kannan, Anitha and Winn, John and Carsten Rother}
}
@conference {Kannan2007,
	title = {Clustering appearance and shape by learning jigsaws},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2007},
	pages = {657{\textendash}664},
	abstract = {Patch-based appearance models are used in a wide range of computer vision applications. To learn such models it has previously been necessary to specify a suitable set of patch sizes and shapes by hand. In the jigsaw model presented here, the shape, size and appearance of patches are learned automatically from the repeated structures in a set of training images. By learning such irregularly shaped {\textquoteright}jigsaw pieces{\textquoteright}, we are able to discover both the shape and the appearance of object parts without supervision. When applied to face images, for example, the learned jigsaw pieces are surprisingly strongly associated with face parts of different shapes and scales such as eyes, noses, eyebrows and cheeks, to name a few. We conclude that learning the shape of the patch not only improves the accuracy of appearance-based part detection but also allows for shape-based part detection. This enables parts of similar appearance but different shapes to be distinguished; for example, while foreheads and cheeks are both skin colored, they have markedly different shapes.},
	isbn = {9780262195683},
	issn = {10495258},
	doi = {10.7551/mitpress/7503.003.0087},
	author = {Kannan, Anitha and Winn, John and Carsten Rother}
}
@conference {Lalonde2007,
	title = {Photo clip art},
	booktitle = {Proceedings of the ACM SIGGRAPH Conference on Computer Graphics},
	year = {2007},
	abstract = {We present a system for inserting new objects into existing photographs by querying a vast image-based object library, pre-computed using a publicly available Internet object database. The central goal is to shield the user from all of the arduous tasks typically involved in image compositing. The user is only asked to do two simple things: 1) pick a 3D location in the scene to place a new object; 2) select an object to insert using a hierarchical menu. We pose the problem of object insertion as a data-driven, 3D-based, context-sensitive object retrieval task. Instead of trying to manipulate the object to change its orientation, color distribution, etc. to fit the new image, we simply retrieve an object of a specified class that has all the required properties (camera pose, lighting, resolution, etc) from our large object library. We present new automatic algorithms for improving object segmentation and blending, estimating true 3D object size and orientation, and estimating scene lighting conditions. We also present an intuitive user interface that makes object insertion fast and simple even for the artistically challenged. {\textcopyright} 2007 ACM.},
	keywords = {3D scene reasoning, Blending and compositing, Computational photography, Image databases, Object insertion},
	doi = {10.1145/1275808.1276381},
	url = {http://graphics.cs.cmu.edu/projects/photoclipart/},
	author = {Lalonde, Jean Fran{\c c}ois and Hoiem, Derek and Efros, Alexei A and Carsten Rother and Winn, John and Criminisi, Antonio}
}
@conference {Shotton2006,
	title = {TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {3951 LNCS},
	year = {2006},
	pages = {1{\textendash}15},
	abstract = {This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned model is used for automatic visual recognition and semantic segmentation of photographs. Our discriminative model exploits novel features, based on textons, which jointly model shape and texture. 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 these classifiers in a conditional random field. Efficient training of the model on very large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy are demonstrated on three different databases: i) our own 21-object class database of 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 [1]. The proposed algorithm gives competitive results both for highly textured (e.g. grass, trees), highly structured (e.g. cars, faces, bikes, aeroplanes) and articulated objects (e.g. body, cow). {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {aeroplanes) and articulated objects (eg body, bikes, cow), faces, g grass, highly structured (eg cars, trees)},
	isbn = {3540338322},
	issn = {16113349},
	doi = {10.1007/11744023_1},
	url = {http://research.microsoft.com/vision/cambridge/recognition/.},
	author = {Shotton, Jamie and Winn, John and Carsten Rother and Criminisi, Antonio}
}
