Web-Scale Responsive Visual Search At Bing

Authors:
Houdong Hu Microsoft
Yan Wang Microsoft
Linjun Yang Microsoft
Pavel Komlev Microsoft
Li Huang Microsoft
Xi Stephen Chen Microsoft
Jiapei Huang Microsoft
Ye Wu Microsoft
Meenaz Merchant Microsoft
Arun Sacheti Microsoft

Introduction:

In this paper, the authors introduce a web-scale general visual search system deployed in Microsoft Bing.

Abstract:

In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing. The system accommodates tens of billions of images in the index, with thousands of features for each image, and can respond in less than 200 ms. In order to overcome the challenges in relevance, latency, and scalability in such large scale of data, we employ a cascaded learning-to-rank framework based on various latest deep learning visual features, and deploy in a distributed heterogeneous computing platform. Quantitative and qualitative experiments show that our system is able to support various applications on Bing website and apps.

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