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基本图像检索和结构预测的潜在变量模型
Latent Variable Models for Content-Based Image Retrieval and Structure Prediction |
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| 课程网址: |
http://videolectures.net/bmvc2012_quattoni_structure_prediction/
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| 主讲教师: |
Ariadna Quattoni
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| 开课单位: |
加泰罗尼亚理工大学
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| 开课时间: |
2012-10-09
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| 课程语种: |
英语
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| 中文简介: |
在第一部分，我将介绍最近的工作学习潜在变量模型的内容为基础的图像检索。要了解一个预测数据库图像与图像查询相关性的函数，我们所需要的只是来自检索系统用户的某种形式的反馈。例如，我们可以得到三重约束，指定相对于某些查询Q，图像A的排名应该高于图像B。当这种反馈可用时，可以使用排名SVMS来诱导检索函数。我将描述这个框架的扩展，在这个框架中，我们学习的不是单一的相关函数，而是混合的相关函数。直观地说，给定一个查询，我们首先计算粗隐类的分布，然后计算该类查询的相关函数。我将提出一个简单的学习算法，诱导潜在类和每个模型的参数。在本文的第二部分中，我将描述我目前在开发有效的学习算法以预测潜在变量的结构方面所做的一些工作。这些算法基于直接利用分布的马尔可夫性的代数表示。
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| 课程简介: |
In the first part of the talk I will present recent work on learning latent variable models for content-based image retrieval. To learn a function that predicts the relevance of a database image to an image query all that we need is some form of feedback from users of the retrieval system. For example, we can obtain triplet constraints specifying that relative to some query Q, an image A should be ranked higher than an image B. When such feedback is available ranking SVMs can be used to induce the retrieval function. I will describe an extension of this framework where instead of learning a single relevance function we learn a mixture of relevance functions. Intuitively, given a query we first compute a distribution over "coarse" latent classes and then compute the relevance function for queries of that class. I will present a simple learning algorithm that induces both the latent classes and the parameters of each model. In the second part of the talk I will describe some of my current work on developing efficient learning algorithms for structure prediction with latent variables. These algorithms are based on using an algebraic representation that exploits directly the markovianity of the distribution.
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| 关 键 词: |
变量; 函数; 代数
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-12-20：lxf
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| 阅读次数: |
52
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