Mixer: Efficiently Understanding and Retrieving Visual Content at Web-Scale
Mixer: Efficiently Understanding and Retrieving Visual Content at Web-Scale
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DOI：
10.14778/3476311.3476371
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Mengbai Xiao;An Qin;Yongwei Wu;Xinjie Huang;Xiaodong Zhang
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文献类型：
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作者：
Mengbai Xiao;An Qin;Yongwei Wu;Xinjie Huang;Xiaodong Zhang
如今，包括图像和视频在内的视觉内容主要是在互联网上进行的。一个有效的系统来识别和分析视觉内容并提取其功能以进行数据重新设计，旨在解决两个关键问题：（1）有效，及时理解视觉（2）在不损害混音器中的性能的情况下，将它们归为不同的类别层是为图像和视频设计的，我们能够以有效的方式对两种类型进行聚合检索Baidu的生产工作负载和系统表明，混音器将模型生产时间减少，并将功能生产量提高到9.14倍，也可以达到95％和97％的视频检索。 ，这使搜索引擎以低成本的视觉内容高度扩展。通常将使其他数据处理应用程序受益。
Visual contents, including images and videos, are dominant on the Internet today. The conventional search engine is mainly designed for textual documents, which must be extended to process and manage increasingly high volumes of visual data objects. In this paper, we present Mixer, an effective system to identify and analyze visual contents and to extract their features for data re-trievals, aiming at addressing two critical issues: (1) efficiently and timely understanding visual contents, (2) retrieving them at high precision and recall rates without impairing the performance. In Mixer, the visual objects are categorized into different classes, each of which has representative visual features. Subsystems for model production and model execution are developed. Two retrieval layers are designed and implemented for images and videos, respectively. In this way, we are able to perform aggregation retrievals of the two types in efficient ways. The experiments with Baidu’s production workloads and systems show that Mixer halves the model production time and raises the feature production throughput by 9.14x. Mixer also achieves the precision and recall of video retrievals at 95% and 97%, respectively. Mixer has been in its daily operations, which makes the search engine highly scalable for visual contents at a low cost. Having observed productivity improvement of upper-level applications in the search engine, we believe our system framework would generally benefit other data processing applications.