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Harvesting Social Images for Bi-Concept Search

机译：收集社交图像以进行双概念搜索

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摘要

Searching for the co-occurrence of two visual concepts in unlabeled images is an important step towards answering complex user queries. Traditional visual search methods use combinations of the confidence scores of individual concept detectors to tackle such queries. In this paper we introduce the notion of bi-concepts, a new concept-based retrieval method that is directly learned from social-tagged images. As the number of potential bi-concepts is gigantic, manually collecting training examples is infeasible. Instead, we propose a multimedia framework to collect de-noised positive as well as informative negative training examples from the social web, to learn bi-concept detectors from these examples, and to apply them in a search engine for retrieving bi-concepts in unlabeled images. We study the behavior of our bi-concept search engine using 1.2 M social-tagged images as a data source. Our experiments indicate that harvesting examples for bi-concepts differs from traditional single-concept methods, yet the examples can be collected with high accuracy using a multi-modal approach. We find that directly learning bi-concepts is better than oracle linear fusion of single-concept detectors, with a relative improvement of 100%. This study reveals the potential of learning high-order semantics from social images, for free, suggesting promising new lines of research.
机译：在未标记图像中搜索两个视觉概念的同时出现是迈向回答复杂用户查询的重要一步。传统的视觉搜索方法使用各个概念检测器的置信度得分的组合来解决此类查询。在本文中，我们介绍了双向概念的概念，双向概念是一种新的基于概念的检索方法，可以直接从带有社会标签的图像中学习。由于潜在的双概念的数量巨大，因此手动收集培训示例是不可行的。取而代之的是，我们提出了一个多媒体框架，从社交网络中收集经过消噪处理的积极和有益的消极训练示例，从这些示例中学习双概念检测器，并将其应用于搜索引擎中以检索未标记的双概念图片。我们使用120万个带有社会标签的图像作为数据源，研究了双概念搜索引擎的行为。我们的实验表明，双概念的收获示例不同于传统的单概念方法，但是可以使用多模式方法以高精度收集示例。我们发现，直接学习双概念比单概念检测器的oracle线性融合更好，相对改善了100％。这项研究揭示了免费从社交图像中学习高级语义的潜力，这表明有希望的新研究领域。

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