Attention-Guided Organized Perception and Learning of Object Categories Based on Probabilistic Latent Variable Models
Attention-Guided Organized Perception and Learning of Object Categories Based on Probabilistic Latent Variable Models
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DOI：
10.4236/jilsa.2013.52014
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发表时间：
2013-05
期刊：
影响因子：
--
通讯作者：
M. Atsumi
中科院分区：
文献类型：
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作者：
M. Atsumi
本文提出了一个概率模型的对象类别学习与注意力引导的有组织的感知。该模型由一个模型的注意力引导的有组织的感知对象段的马尔可夫随机场和一个模型的学习对象类别的基础上的概率潜在成分分析。在注意引导的有组织感知中，在显著的前注意点周围的动态形成的马尔可夫随机场上执行并发图形-背景分割，并且将共现片段分组在选择性注意片段的邻域中。在对象类别学习中，每个对象类别的类的集合是基于概率潜在成分分析获得的，具有来自从包含上下文中的类别对象的图像中提取的片段的特征包的可变数目的类，并且对象类别由对象类别的组合表示。通过两组图像数据的实验表明，该模型学习了对象类别的类内成分和类间差异的概率结构，在对象类别识别中取得了很好的性能。
This paper proposes a probabilistic model of object category learning in conjunction with attention-guided organized perception. This model consists of a model of attention-guided organized perception of object segments on Markov random fields and a model of learning object categories based on a probabilistic latent component analysis. In attention guided organized perception, concurrent figure-ground segmentation is performed on dynamically-formed Markov random fields around salient preattentive points and co-occurring segments are grouped in the neighborhood of selective attended segments. In object category learning, a set of classes of each object category is obtained based on the probabilistic latent component analysis with the variable number of classes from bags of features of segments extracted from images which contain the categorical objects in context and an object category is represented by a composite of object classes. Through experiments using two image data sets, it is shown that the model learns a probabilistic structure of intra-categorical composition and inter-categorical difference of object categories and achieves high performance in object category recognition.