Computer Vision meets Databases最新文献

A case-study of scoring schemes for the PvS-index pvs指数评分方案的案例研究

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160953

Herwig Lejsek

{"title":"A case-study of scoring schemes for the PvS-index","authors":"Herwig Lejsek","doi":"10.1145/1160939.1160953","DOIUrl":"https://doi.org/10.1145/1160939.1160953","url":null,"abstract":"Recently we have proposed a new indexing method for high-dimensional data, the PvS-index. It provides fast query processing in constant time and is well suited for doing similarity search in Image Retrieval Systems using local descriptors. It is based on projecting data points onto random lines and uses this information to segment them into appropriately sized buckets, which can be read in just one I/O operation. After this preprocessing step the search queries just three buckets per query descriptor and uses a recent rank aggregation method, OMEDRANK, in order to provide good approximate results for the nearest neighbour problem.We have recently shown that PvS-indexing works well for large collections of real image data. In that work, however, we used a simple scoring scheme and collected few nearest neighbours for each query descriptor. In this study we examine how much the actual number of nearest neighbours, gathered for each local descriptor, influences the final query result, when searching a PvS-index. Based on the results we propose two new alternative scoring schemes, which improve the retrieval quality and stabilise the results, making the search less affected by the actual number of nearest neighbours accumulated.","PeriodicalId":346313,"journal":{"name":"Computer Vision meets Databases","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123691593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Data embedding techniques and applications 数据嵌入技术及应用

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160948

Li Yang

引用次数: 5

On the impact of outliers on high-dimensional data analysis methods for face recognition 异常值对人脸识别高维数据分析方法的影响

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160952

Sid-Ahmed Berrani, Christophe Garcia

引用次数: 5

Optimizing progressive query-by-example over pre-clustered large image databases 在预聚类的大型图像数据库上优化逐例查询

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160946

A. Choupo, Laure Berti-Équille, A. Morin

{"title":"Optimizing progressive query-by-example over pre-clustered large image databases","authors":"A. Choupo, Laure Berti-Équille, A. Morin","doi":"10.1145/1160939.1160946","DOIUrl":"https://doi.org/10.1145/1160939.1160946","url":null,"abstract":"The typical mode for querying in an image content-based information system is query-by-example, which allows the user to provide an image as a query and to search for similar images (i.e., the nearest neighbors) based on one or a combination of low-level multidimensional features of the query example. Off-lime, this requires the time-consuming pre-computing of the whole set of visual descriptors over the image database. On-line, one major drawback is that multidimensional sequential NN-search is usually exhaustive over the whole image set face to the user who has a very limited patience. In this paper, we propose a technique for improving the performance of image query-by-example execution strategies over multiple visual features. This includes first, the pre-clustering of the large image database and then, the scheduling of the processing of the feature clusters before providing progressively the query results (i.e., intermediate results are sent continuously before the end of the exhaustive scan over the whole database). A cluster eligibility criterion and two filtering rules are proposed to select the most relevant clusters to a query-by-example. Experiments over more than 110 000 images and five MPEG-7 global features show that our approach significantly reduces the query time in two experimental cases: the query time is divided by 4.8 for 100 clusters per descriptor type and by 7 for 200 clusters per descriptor type compared to a \"blind\" sequential NN-search with keeping the same final query result. This constitutes a promising perspective for optimizing image query-by-example execution.","PeriodicalId":346313,"journal":{"name":"Computer Vision meets Databases","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128794120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

A fast shot matching strategy for detecting duplicate sequences in a television stream 一种检测电视流中重复序列的快速镜头匹配策略

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160947

Xavier Naturel, P. Gros

引用次数: 39

Joint exploitation of multiple media from multimedia to databases 从多媒体到数据库的多种媒体的联合开发

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160943

P. Gros

{"title":"Joint exploitation of multiple media from multimedia to databases","authors":"P. Gros","doi":"10.1145/1160939.1160943","DOIUrl":"https://doi.org/10.1145/1160939.1160943","url":null,"abstract":"Multimedia content analysis offers many exciting research opportunities and is a necessary step towards automatic understanding of the content of digital documents. Digital documents are typically composite. Processing in parallel and integrating low-level information computed over each of the media that compose a multimedia document can yield knowledge that stand-alone and isolated analysis could not discover.Joint processing of multiple media is very challenging, even at the lowest analysis levels. Coping with imperfect synchronization of pieces of information, mixing extremely different kinds of information (numerical or symbolic descriptions, values describing intervals or instants, probabilities and distances, HMM and Gaussians, ...), and reconciling contradictory outputs are some of the obstacles which make processing of multimedia documents much more difficult than it seems at first glance.This talk will first show what may be gained from jointly analyzing multimedia documents. It will then briefly overview the typical information that can be extracted from major media (video, sound, images and text) before focusing on the problems that arise when trying to use all this information together. We hope to convince researchers to start trying to solve these problems, since they directly hamper the acquisition of higher-level knowledge from multimedia documents.","PeriodicalId":346313,"journal":{"name":"Computer Vision meets Databases","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130050766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using pivots to index for support vector machine queries 使用枢轴索引支持向量机查询

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160954

Arun Qamra, E. Chang

引用次数: 2

Similarity search in high-dimensional datasets 高维数据集的相似度搜索

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160941

R. Ramakrishnan, J. Goldstein, U. Shaft

{"title":"Similarity search in high-dimensional datasets","authors":"R. Ramakrishnan, J. Goldstein, U. Shaft","doi":"10.1145/1160939.1160941","DOIUrl":"https://doi.org/10.1145/1160939.1160941","url":null,"abstract":"The problem of finding \"similar\" multimedia objects is a central one, and a popular approach is to represent objects as vectors in a high-dimensional space, and to build a spatial index over a collection of such vectors in order to retrieve the \"nearest neighbors\" of a query object. There are some fundamental assumptions involved here. First, that the user's notion of similarity can be captured by distance in the space that the vectors are embedded, and second, that nearest neighbors can be efficiently retrieved. In this talk, we discuss these assumptions, based on our experience with the PiQ image database project, carried out at the University of Wisconsin-Madison, and some subsequent work.We will first present a brief overview of the PiQ system and its goal of identifying the DBMS infrastructure required to support image databases, and discuss the role of similarity and nearest-neighbor queries in content-based querying. Next, we consider when the notion of \"nearest neighbor\" is well-defined in high-dimensional spaces, and when efficient indexing is feasible. The goal is not to suggest that indexing high-dimensional data is impossible, although our results here are mainly negative. Rather, we seek to identify the conditions under which effective indexing and retrieval techniques are feasible, and to identify the key difficulties that must be overcome. Finally, we present some indexing techniques to retrieve nearest neighbors under appropriate conditions, highlighting the role played by redundancy and approximation.","PeriodicalId":346313,"journal":{"name":"Computer Vision meets Databases","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115308549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimedia applications: beyond similarity searches 多媒体应用:超越相似性搜索

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160957

John R. Smith, D. Doermann, Amarnath Gupta, J. Goldstein, U. Shaft, N. Ratha

引用次数: 0

EXTENT: fusing context, content, and semantic ontology for photo annotation EXTENT：融合上下文、内容和语义本体进行照片注释

Computer Vision meets Databases Pub Date : 2005-06-17 DOI: 10.1145/1160939.1160945

E. Chang

引用次数: 15