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首页> 外文期刊>International Journal of Performability Engineering >Video Retrieval and Sorting Algorithm based on Multiple Features in Sports Videos
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Video Retrieval and Sorting Algorithm based on Multiple Features in Sports Videos

机译：基于体育视频多个功能的视频检索与分类算法

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摘要

Multimedia information, especially videos, is growing explosively with the rapid development of the Internet and multimedia technology. Due to its variety of image features, it is capable of reaching several hundred dimensions and even thousands of dimensions. Storing and indexing the high-dimensional feature vectors has become key technologies of content-based video retrieval. The residual quantization mechanism, which combines the asymmetric distance and set sorting algorithm based on multi-feature candidates, is improved after analyzing the characteristics of soccer videos. For soccer videos, SD-VLAD (Soft Distribution-Vectors of Locally Aggregated Descriptors), BOC (Bag of Color), and shot type are selected for describing the information of images. To address the problem that the original residual quantized inverted index can only retrieve single features, multiple feature retrieval and sorting are proposed. In the stage of candidate set sorting, a multi-feature based similarity calculation method is designed according to the shots type. The experimental results show that multi-feature hierarchical retrieval and sorting can be achieved at the cost of memory space. While ensuring query speed, the accuracy of the query is improved.
机译：多媒体信息，尤其是视频，随着互联网和多媒体技术的快速发展，易于发展。由于其种类的图像特征，它能够达到数百个维度甚至数千个尺寸。存储和索引高维特征向量已成为基于内容的视频检索的关键技术。在分析足球视频的特征之后，改进了基于多特征候选的非特征候选的非对称距离和设定分类算法的残余量化机制。对于足球视频，SD-VLAD（局部聚合描述符的软分配矢量），选择BOC（颜色袋）和拍摄类型，用于描述图像的信息。为了解决原始残余量化反相索引只能检索单个特征的问题，提出了多个特征检索和分类。在候选集排序的阶段，根据截图类型设计了一种基于多特征的相似性计算方法。实验结果表明，可以以存储空间的成本实现多特征层次检索和分类。在确保查询速度的同时，提高了查询的准确性。

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