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Graph-based semi-supervised learning with Local Binary Patterns for holistic object categorization

机译：基于图的半监督学习与局部二进制模式进行整体对象分类

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摘要

In this paper, we develop a new efficient graph construction algorithm that is useful for many learning tasks. Unlike the main stream for graph construction, our proposed data self-representativeness approach simultaneously estimates the graph structure and its edge weights through sample coding. Compared with the recent (e_1) graph based on sparse coding, our proposed objective function has an analytical solution (based on self-representativeness of data) and thus is more efficient. This paper has two main contributions. Firstly, we introduce a principled Two Phase Weighted Regularized Least Square graph construction method. Secondly, the obtained data graph is used, in a semi-supervised context, in order to categorize detected objects in outdoor and indoor scenes using Local Binary Patterns as image descriptors. In many previous works, LBP descriptors (histograms) were used as feature vectors for object detection and recognition. However, our work exploits them in order to construct adaptive graphs using a self-representativeness coding. The experiments show that the proposed method can outperform competing methods.
机译：在本文中，我们开发了一种新的高效图构建算法，该算法可用于许多学习任务。与用于图形构建的主流不同，我们提出的数据自表示方法通过样本编码同时估计图形结构及其边缘权重。与最近基于稀疏编码的（e_1）图相比，我们提出的目标函数具有解析解决方案（基于数据的自表示性），因此效率更高。本文有两个主要贡献。首先，我们介绍一种有原则的两阶段加权正则化最小二乘图构造方法。其次，在半监督的上下文中使用获得的数据图，以使用本地二进制模式作为图像描述符对室外和室内场景中检测到的对象进行分类。在许多以前的工作中，LBP描述符（直方图）被用作目标检测和识别的特征向量。但是，我们的工作是利用它们来利用自表示编码来构造自适应图。实验表明，所提出的方法优于竞争方法。

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