Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms.

中文翻译：

---

基于高级语义假设生成的场景分类多视图主动学习

多视图主动学习（MVAL）是一种与传统主动学习相比可以大幅度减小版本空间大小的技术，在大规模数据分析中具有巨大的应用潜力。本文研究了基于MVAL的场景分类，帮助计算机从宏观上准确理解多样复杂的环境，已广泛应用于图像检索、自动驾驶等多个领域。本文的主要贡献是使用不同的高层图像语义替代传统的低层特征，从而在 MVAL 中生成更加独立和多样化的假设。首先，我们的算法使用不同的对象检测器来实现场景中的局部对象响应。此外，我们设计了一个级联在线 LDA 模型来挖掘图像的主题语义。