摘要翻译：
视频中标识的频度和持续时间的估计是广告业中重要且具有挑战性的一种估计广告购买影响的方法。由于标识在视频中只占很小的区域，现有的图像检索方法可能会失效。本文提出了一种视频标志检索(VLR)算法，它是一种基于局部图像描述符的空间分布的图像到视频检索算法，该描述符度量查询图像（标志）与视频图像集合之间的距离。VLR利用局部特征克服了卷积神经网络(CNN)等基于全局特征的模型的缺点。同时VLR具有灵活性，在设置一些超参数后不需要训练。在两个具有挑战性的开放基准任务（SoccerNet和Standford I2V)上评估了VLR的性能，并与其他最先进的标志检索或检测算法进行了比较。总体而言，VLR比现有的方法显示出明显更高的准确性。
---
英文标题：
《Video Logo Retrieval based on local Features》
---
作者：
Bochen Guan, Hanrong Ye, Hong Liu, William A. Sethares
---
最新提交年份：
2020
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--
一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--

---
英文摘要：
  Estimation of the frequency and duration of logos in videos is important and challenging in the advertisement industry as a way of estimating the impact of ad purchases. Since logos occupy only a small area in the videos, the popular methods of image retrieval could fail. This paper develops an algorithm called Video Logo Retrieval (VLR), which is an image-to-video retrieval algorithm based on the spatial distribution of local image descriptors that measure the distance between the query image (the logo) and a collection of video images. VLR uses local features to overcome the weakness of global feature-based models such as convolutional neural networks (CNN). Meanwhile, VLR is flexible and does not require training after setting some hyper-parameters. The performance of VLR is evaluated on two challenging open benchmark tasks (SoccerNet and Standford I2V), and compared with other state-of-the-art logo retrieval or detection algorithms. Overall, VLR shows significantly higher accuracy compared with the existing methods.
---
PDF链接：
https://arxiv.org/pdf/1808.03735