{"title":"基于压缩费雪向量的视频目标检索","authors":"Luca Bertinetto, A. Fiandrotti, E. Magli","doi":"10.5281/ZENODO.44154","DOIUrl":null,"url":null,"abstract":"This paper addresses the problem of retrieving those shots from a database of video sequences that match a query image. Existing architectures match the images using a high-level representation of local features extracted from the video database, and are mainly based on Bag ofWords model. Such architectures lack however the capability to scale up to very large databases. Recently, Fisher Vectors showed promising results in large scale image retrieval problems, but it is still not clear how they can be best exploited in video-related applications. In our work, we use compressed Fisher Vectors to represent the video shots and we show that inherent correlation between video frames can be effectively exploited. Experiments show that our proposed system achieves better performance while having lower computational requirements than similar architectures.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Shot-based object retrieval from video with compressed Fisher Vectors\",\"authors\":\"Luca Bertinetto, A. Fiandrotti, E. Magli\",\"doi\":\"10.5281/ZENODO.44154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper addresses the problem of retrieving those shots from a database of video sequences that match a query image. Existing architectures match the images using a high-level representation of local features extracted from the video database, and are mainly based on Bag ofWords model. Such architectures lack however the capability to scale up to very large databases. Recently, Fisher Vectors showed promising results in large scale image retrieval problems, but it is still not clear how they can be best exploited in video-related applications. In our work, we use compressed Fisher Vectors to represent the video shots and we show that inherent correlation between video frames can be effectively exploited. Experiments show that our proposed system achieves better performance while having lower computational requirements than similar architectures.\",\"PeriodicalId\":198408,\"journal\":{\"name\":\"2014 22nd European Signal Processing Conference (EUSIPCO)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 22nd European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.44154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 22nd European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.44154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
摘要
本文解决了从与查询图像匹配的视频序列数据库中检索这些镜头的问题。现有的体系结构主要基于Bag of words模型,使用从视频数据库中提取的局部特征的高级表示来匹配图像。然而,这种架构缺乏扩展到非常大的数据库的能力。最近,Fisher Vectors在大规模图像检索问题上显示了有希望的结果,但如何在视频相关应用中最好地利用它们仍然不清楚。在我们的工作中,我们使用压缩的Fisher向量来表示视频镜头,我们表明视频帧之间的内在相关性可以有效地利用。实验表明,与同类体系结构相比,我们的系统具有更好的性能和更低的计算需求。
Shot-based object retrieval from video with compressed Fisher Vectors
This paper addresses the problem of retrieving those shots from a database of video sequences that match a query image. Existing architectures match the images using a high-level representation of local features extracted from the video database, and are mainly based on Bag ofWords model. Such architectures lack however the capability to scale up to very large databases. Recently, Fisher Vectors showed promising results in large scale image retrieval problems, but it is still not clear how they can be best exploited in video-related applications. In our work, we use compressed Fisher Vectors to represent the video shots and we show that inherent correlation between video frames can be effectively exploited. Experiments show that our proposed system achieves better performance while having lower computational requirements than similar architectures.