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Textual Query of Personal Photos Facilitated by Large-Scale Web Data

机译：大规模Web数据促进的个人照片文本查询

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摘要

The rapid popularization of digital cameras and mobile phone cameras has led to an explosive growth of personal photo collections by consumers. In this paper, we present a real-time textual query-based personal photo retrieval system by leveraging millions of Web images and their associated rich textual descriptions (captions, categories, etc.). After a user provides a textual query (e.g., "waterȁD;), our system exploits the inverted file to automatically find the positive Web images that are related to the textual query "waterȁD; as well as the negative Web images that are irrelevant to the textual query. Based on these automatically retrieved relevant and irrelevant Web images, we employ three simple but effective classification methods, k-Nearest Neighbor (kNN), decision stumps, and linear SVM, to rank personal photos. To further improve the photo retrieval performance, we propose two relevance feedback methods via cross-domain learning, which effectively utilize both the Web images and personal images. In particular, our proposed cross-domain learning methods can learn robust classifiers with only a very limited amount of labeled personal photos from the user by leveraging the prelearned linear SVM classifiers in real time. We further propose an incremental cross-domain learning method in order to significantly accelerate the relevance feedback process on large consumer photo databases. Extensive experiments on two consumer photo data sets demonstrate the effectiveness and efficiency of our system, which is also inherently not limited by any predefined lexicon.
机译：数码相机和手机相机的迅速普及导致消费者个人照片收藏的爆炸性增长。在本文中，我们通过利用数百万个Web图像及其相关的丰富文本描述（标题，类别等），提供了一个基于实时文本查询的个人照片检索系统。用户提供文字查询（例如“waterȁD;”）后，我们的系统会利用反向文件自动找到与文字查询“waterȁD;以及与文字查询无关的负面Web图片。基于这些自动检索的相关和不相关的Web图像，我们采用三种简单但有效的分类方法，即k最近邻（kNN），决策树桩和线性SVM，对个人照片进行排名。为了进一步提高照片检索性能，我们提出了两种通过跨域学习的相关性反馈方法，可以有效地利用Web图像和个人图像。特别地，我们提出的跨域学习方法可以通过实时利用预学习的线性SVM分类器，仅从用户那里获取非常有限的带标签个人照片，从而学习鲁棒的分类器。我们还提出了一种增量跨域学习方法，以显着加速大型消费者照片数据库上的相关性反馈过程。在两个消费者照片数据集上进行的大量实验证明了我们系统的有效性和效率，这也固有地不受任何预定义词典的限制。

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