Titlebook: Extreme Value Theory-Based Methods for Visual Recognition; Walter J. Scheirer Book 2017 Springer Nature Switzerland AG 2017

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Technology, Development, and Resourcesking algorithms, a distance or similarity score is at the heart of their learning objective. The typical training process involves an assessment stage where a feature vector . is classified by the current iteration of a measurable recognition function ., and the resulting score . is checked against

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https://doi.org/10.1007/978-94-011-0655-9g with the foundation we laid in Chapters 1 and 2, we learned how EVT differs from central tendency modeling, which is the dominant mode of modeling in computer vision. With a general statistical paradigm that is well suited to modeling decision boundaries, which we hypothesize are defined by extrem

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Synthesis Lectures on Computer Visionhttp://image.papertrans.cn/f/image/320066.jpg

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A Brief Introduction to Statistical Extreme Value Theory,e distribution to be modeled consists of extrema. As emphasized above in Chapter 1, extrema are the minima or maxima sampled from an overall distribution of data. To quote Coles [2001] “The distinguishing feature of an extreme value analysis is the objective to quantify the stochastic behavior of a

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Recognition Score Normalization,ame type of sensor), while others may not be (e.g., a collection of different classifiers, trained over different feature spaces). How we combine heterogeneous information has a major impact on the final decision for our recognition task. Remarkably, often little to no consideration is given to this