Structured learning and prediction in computer vision

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structured learning and prediction in computer vision

Christoph H. Lampert (Author of Structured Learning and Prediction in Computer Vision)

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Published 04.01.2019

Structured Output Prediction 1 - Thomas Hofmann - MLSS 2013 Tübingen

Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms

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Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than scalar discrete or real values. Similar to commonly used supervised learning techniques, structured prediction models are typically trained by means of observed data in which the true prediction value is used to adjust model parameters. Due to the complexity of the model and the interrelations of predicted variables the process of prediction using a trained model and of training itself is often computationally infeasible and approximate inference and learning methods are used. For example, the problem of translating a natural language sentence into a syntactic representation such as a parse tree can be seen as a structured prediction problem [2] in which the structured output domain is the set of all possible parse trees. Structured prediction is also used in a wide variety of application domains including bioinformatics , natural language processing , speech recognition , and computer vision. Sequence tagging is a class of problems prevalent in natural language processing , where input data are often sequences e. The sequence tagging problem appears in several guises, e.

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In this paper, we explore the use of deep convolution and deep belief networks as potential functions in structured prediction models for the segmentation of breast masses from mammograms. In particular, the structured prediction models are estimated with loss minimization parameter learning algorithms, representing: a conditional random field CRF , and b structured support vector machine SSVM. For the CRF model, we use the inference algorithm based on tree re-weighted belief propagation with truncated fitting training, and for the SSVM model the inference is based on graph cuts with maximum margin training. We show empirically the importance of deep learning methods in producing state-of-the-art results for both structured prediction models. Finally, we show that the CRF model is significantly faster than SSVM, both in terms of inference and training time, which suggests an advantage of CRF models when combined with deep learning potential functions.

Simply link your Qantas Frequent Flyer membership number to your Booktopia account and earn points on eligible orders. Either by signing into your account or linking your membership details before your order is placed. Your points will be added to your account once your order is shipped. Click on the cover image above to read some pages of this book! Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints.

4 thoughts on “Christoph H. Lampert (Author of Structured Learning and Prediction in Computer Vision)

  1. classes of structured models in computer vision. Our focus is discrete based methods from the more recent prediction-based parameter learning methods.

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