To implement fine-grained context recognition that is accurate and affordable for general households, we present a novel technique that integrates multiple image-based cognitive APIs and light-weight machine learning. Our key idea is to regard every image as a document by exploiting "tags" derived by multiple APIs. The aim of this paper is to compare API-based models' performance and improve the recognition accuracy by preserving the affordability for general households. We present a novel method for further improving the recognition accuracy based on multiple cognitive APIs and four modules, fork integration, majority voting, score voting, and range voting.

中文翻译：

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使用“文档图像”方法集成多个模型以识别细粒度的家庭环境。

为了实现对普通家庭而言准确且价格合理的细粒度上下文识别，我们提出了一种新颖的技术，该技术集成了多个基于图像的认知API和轻量级机器学习。我们的关键思想是通过利用多个API派生的“标签”将每张图像视为一个文档。本文的目的是比较基于API的模型的性能，并通过保留普通家庭的负担能力来提高识别准确性。我们提出了一种基于多个认知API和四个模块，分叉集成，多数表决，得分表决和范围表决的进一步提高识别准确性的新颖方法。