Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献
Mirwaes Wahabzada, K. Kersting, A. Pilz, C. Bauckhage
There have recently been considerable advances in fast inference for (online) latent Dirichlet allocation (LDA). While it is widely recognized that the scheduling of documents in stochastic optimization and in turn in LDA may have significant consequences, this issue remains largely unexplored. Instead, practitioners schedule documents essentially uniformly at random, due perhaps to ease of implementation, and to the lack of clear guidelines on scheduling the documents. In this work, we address this issue and propose to schedule documents for an update that exert a disproportionately large influence on the topics of the corpus before less influential ones. More precisely, we justify to sample documents randomly biased towards those ones with higher norms to form mini-batches. On several real-world datasets, including 3M articles from Wikipedia and 8M from PubMed, we demonstrate that the resulting influence scheduled LDA can handily analyze massive document collections and find topic models as good or better than those found with online LDA, often at a fraction of time.
{"title":"More influence means less work: fast latent dirichlet allocation by influence scheduling","authors":"Mirwaes Wahabzada, K. Kersting, A. Pilz, C. Bauckhage","doi":"10.1145/2063576.2063944","DOIUrl":"https://doi.org/10.1145/2063576.2063944","url":null,"abstract":"There have recently been considerable advances in fast inference for (online) latent Dirichlet allocation (LDA). While it is widely recognized that the scheduling of documents in stochastic optimization and in turn in LDA may have significant consequences, this issue remains largely unexplored. Instead, practitioners schedule documents essentially uniformly at random, due perhaps to ease of implementation, and to the lack of clear guidelines on scheduling the documents.\u0000 In this work, we address this issue and propose to schedule documents for an update that exert a disproportionately large influence on the topics of the corpus before less influential ones. More precisely, we justify to sample documents randomly biased towards those ones with higher norms to form mini-batches. On several real-world datasets, including 3M articles from Wikipedia and 8M from PubMed, we demonstrate that the resulting influence scheduled LDA can handily analyze massive document collections and find topic models as good or better than those found with online LDA, often at a fraction of time.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"27 1","pages":"2273-2276"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74125151","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}
Wikipedia's rich category structure has helped make it one of the largest semantic taxonomies in existence, a property that has been central to much recent research. However, Wikipedia's category representation is simplistic: an article contains a single list of categories, with no data about their relative importance. We investigate the ordering of category lists to determine how a category's position in the list correlates with its relevance to the article and overall significance. We identify a number of interesting connections between a category's position and its persistence within the article, age, popularity, size, and descriptiveness.
{"title":"Examining the \"leftness\" property of Wikipedia categories","authors":"Karl Gyllstrom, Marie-Francine Moens","doi":"10.1145/2063576.2063953","DOIUrl":"https://doi.org/10.1145/2063576.2063953","url":null,"abstract":"Wikipedia's rich category structure has helped make it one of the largest semantic taxonomies in existence, a property that has been central to much recent research. However, Wikipedia's category representation is simplistic: an article contains a single list of categories, with no data about their relative importance. We investigate the ordering of category lists to determine how a category's position in the list correlates with its relevance to the article and overall significance. We identify a number of interesting connections between a category's position and its persistence within the article, age, popularity, size, and descriptiveness.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"38 1","pages":"2309-2312"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74456995","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}
Behavioral targeting (BT) is a widely used technique for online advertising. It leverages information collected on an individual's web-browsing behavior, such as page views, search queries and ad clicks, to select the ads most relevant to user to display. With the proliferation of social networks, it is possible to relate the behavior of individuals and their social connections. Although the similarity among connected individuals are well established (i.e., homophily), it is still not clear whether and how we can leverage the activities of one's friends for behavioral targeting; whether forecasts derived from such social information are more accurate than standard behavioral targeting models. In this paper, we strive to answer these questions by evaluating the predictive power of social data across 60 consumer domains on a large online network of over 180 million users in a period of two and a half months. To our best knowledge, this is the most comprehensive study of social data in the context of behavioral targeting on such an unprecedented scale. Our analysis offers interesting insights into the value of social data for developing the next generation of targeting services.
{"title":"Large-scale behavioral targeting with a social twist","authors":"Kun Liu, Lei Tang","doi":"10.1145/2063576.2063838","DOIUrl":"https://doi.org/10.1145/2063576.2063838","url":null,"abstract":"Behavioral targeting (BT) is a widely used technique for online advertising. It leverages information collected on an individual's web-browsing behavior, such as page views, search queries and ad clicks, to select the ads most relevant to user to display. With the proliferation of social networks, it is possible to relate the behavior of individuals and their social connections. Although the similarity among connected individuals are well established (i.e., homophily), it is still not clear whether and how we can leverage the activities of one's friends for behavioral targeting; whether forecasts derived from such social information are more accurate than standard behavioral targeting models. In this paper, we strive to answer these questions by evaluating the predictive power of social data across 60 consumer domains on a large online network of over 180 million users in a period of two and a half months. To our best knowledge, this is the most comprehensive study of social data in the context of behavioral targeting on such an unprecedented scale. Our analysis offers interesting insights into the value of social data for developing the next generation of targeting services.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"1 1","pages":"1815-1824"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73271046","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}
G. Cleuziou, D. Buscaldi, Vincent Levorato, G. Dias
We present in this paper a new approach for the automatic generation of lexical structures from texts. This tedious task is based on the strong hypothesis that simple statistical observations on textual usages can provide pieces of semantics about the lexicon. Using such "naive" observations only, we propose a (pre)-topological framework to formalize and combine various hypothesis on textual data usages and then to derive a structure similar to usual lexical knowledge basis such as WordNet. In addition we also consider the evaluation problem for obtained lexical structures ; a multi-level evaluation strategy is proposed that measures the fitting between a given reference structure and automatically generated structures on different point of views : intrinsic/structural and application-based points of view. The evaluation strategy is then used to quantify the contribution of the new structuring approach with respect to the corresponding solution proposed by (Sanderson et al. 2000) on two case studies that differs on the domain and the size of the lexicon.
本文提出了一种自动生成文本词汇结构的新方法。这项繁琐的任务基于一个强有力的假设，即对文本用法的简单统计观察可以提供关于词汇的语义。仅使用这种“幼稚”的观察，我们提出了一个(预)拓扑框架来形式化和组合文本数据使用的各种假设，然后派生出类似于通常的词汇知识基础(如WordNet)的结构。此外，我们还考虑了获得的词汇结构的评价问题;提出了一种多层次评价策略，从内在/结构和基于应用的不同角度衡量给定参考结构与自动生成结构之间的拟合程度。然后使用评估策略来量化新结构化方法相对于(Sanderson et al. 2000)在两个不同领域和词典大小的案例研究中提出的相应解决方案的贡献。
{"title":"A pretopological framework for the automatic construction of lexical-semantic structures from texts","authors":"G. Cleuziou, D. Buscaldi, Vincent Levorato, G. Dias","doi":"10.1145/2063576.2063990","DOIUrl":"https://doi.org/10.1145/2063576.2063990","url":null,"abstract":"We present in this paper a new approach for the automatic generation of lexical structures from texts. This tedious task is based on the strong hypothesis that simple statistical observations on textual usages can provide pieces of semantics about the lexicon. Using such \"naive\" observations only, we propose a (pre)-topological framework to formalize and combine various hypothesis on textual data usages and then to derive a structure similar to usual lexical knowledge basis such as WordNet. In addition we also consider the evaluation problem for obtained lexical structures ; a multi-level evaluation strategy is proposed that measures the fitting between a given reference structure and automatically generated structures on different point of views : intrinsic/structural and application-based points of view. The evaluation strategy is then used to quantify the contribution of the new structuring approach with respect to the corresponding solution proposed by (Sanderson et al. 2000) on two case studies that differs on the domain and the size of the lexicon.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"19 1","pages":"2453-2456"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75321984","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}
Statistical modelling of Information Retrieval (IR) systems is a key driving force in the development of the IR field. The goal of this tutorial is to provide a comprehensive and up-to-date introduction to statistical IR modelling. We take a fresh and systematic perspective from the viewpoint of portfolio theory of IR and risk management. A unified treatment and new insights will be given to reflect the recent developments of considering the ranked retrieval results as a whole. Recent research progress in diversification, risk management, and portfolio theory will be covered, in addition to classic methods such as Maron and Kuhns' Probabilistic Indexing, Robertson-Sparck Jones model (and the resulting BM25 formula) and language modelling approaches. The tutorial also reviews the resulting practical algorithms of risk-aware query expansion, diverse ranking, IR metric optimization as well as their performance evaluations. Practical IR applications such as web search, multimedia retrieval, and collaborative filtering are also introduced, as well as discussion of new opportunities for future research and applications that intersect among information retrieval, knowledge management, and databases.
{"title":"Statistical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond","authors":"Jun Wang, Kevyn Collins-Thompson","doi":"10.1145/2063576.2064033","DOIUrl":"https://doi.org/10.1145/2063576.2064033","url":null,"abstract":"Statistical modelling of Information Retrieval (IR) systems is a key driving force in the development of the IR field. The goal of this tutorial is to provide a comprehensive and up-to-date introduction to statistical IR modelling. We take a fresh and systematic perspective from the viewpoint of portfolio theory of IR and risk management. A unified treatment and new insights will be given to reflect the recent developments of considering the ranked retrieval results as a whole. Recent research progress in diversification, risk management, and portfolio theory will be covered, in addition to classic methods such as Maron and Kuhns' Probabilistic Indexing, Robertson-Sparck Jones model (and the resulting BM25 formula) and language modelling approaches. The tutorial also reviews the resulting practical algorithms of risk-aware query expansion, diverse ranking, IR metric optimization as well as their performance evaluations. Practical IR applications such as web search, multimedia retrieval, and collaborative filtering are also introduced, as well as discussion of new opportunities for future research and applications that intersect among information retrieval, knowledge management, and databases.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"13 29 1","pages":"2603-2604"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78662160","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}
Naoki Tani, Danushka Bollegala, N. P. Chandrasiri, Keisuke Okamoto, Kazunari Nawa, S. Iitsuka, Y. Matsuo
We propose Collaborative Exploratory Search (CES), which is an integration of dialog analysis and web search that involves multiparty collaboration to accomplish an exploratory information retrieval goal. Given a real-time dialog between users on a single topic; we define CES as the task of automatically detecting the topic of the dialog and retrieving task-relevant web pages to support the dialog. To recognize the task of the dialog, we apply the Author--Topic model as a topic model. Then, attribute extraction is applied to the dialog to obtain the attributes of the tasks. Finally, a specific search query is generated to identify the task-relevant information. We implement and evaluate the CES system for a commercial in-vehicle conversation. We also develop an iPad application that listens to conversations among users and continuously retrieves relevant web pages. Our experimental results reveal that the proposed method outperforms existing methods, which demonstrates the potential usefulness of collaborative exploratory search with practically usable accuracy levels.
{"title":"Collaborative exploratory search in real-world context","authors":"Naoki Tani, Danushka Bollegala, N. P. Chandrasiri, Keisuke Okamoto, Kazunari Nawa, S. Iitsuka, Y. Matsuo","doi":"10.1145/2063576.2063909","DOIUrl":"https://doi.org/10.1145/2063576.2063909","url":null,"abstract":"We propose Collaborative Exploratory Search (CES), which is an integration of dialog analysis and web search that involves multiparty collaboration to accomplish an exploratory information retrieval goal. Given a real-time dialog between users on a single topic; we define CES as the task of automatically detecting the topic of the dialog and retrieving task-relevant web pages to support the dialog. To recognize the task of the dialog, we apply the Author--Topic model as a topic model. Then, attribute extraction is applied to the dialog to obtain the attributes of the tasks. Finally, a specific search query is generated to identify the task-relevant information. We implement and evaluate the CES system for a commercial in-vehicle conversation. We also develop an iPad application that listens to conversations among users and continuously retrieves relevant web pages. Our experimental results reveal that the proposed method outperforms existing methods, which demonstrates the potential usefulness of collaborative exploratory search with practically usable accuracy levels.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"8 1","pages":"2137-2140"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78398134","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}
Spreadsheets are used by millions of users as a routine all-purpose data management tool. It is now increasingly necessary for external applications and services to consume spreadsheet data. In this paper, we investigate the problem of transforming spreadsheet data to structured formats required by these applications and services. Unlike prior methods, we propose a novel approach in which transformation logic is embedded into a familiar and expressive spreadsheet-like formula mapping language. Popular transformation patterns provided by transformation languages and mapping tools, that are relevant to spreadsheet-based data transformation, are supported in the language via formulas. Consequently, the language avoids cluttering the source spreadsheets with transformations and turns out to be helpful when multiple schemas are targeted. We implemented a prototype and evaluated the benefits of our approach via experiments in a real application. The experimental results confirmed the benefits of our approach.
{"title":"Spreadsheet-based complex data transformation","authors":"Vu Hung, B. Benatallah, Régis Saint-Paul","doi":"10.1145/2063576.2063829","DOIUrl":"https://doi.org/10.1145/2063576.2063829","url":null,"abstract":"Spreadsheets are used by millions of users as a routine all-purpose data management tool. It is now increasingly necessary for external applications and services to consume spreadsheet data. In this paper, we investigate the problem of transforming spreadsheet data to structured formats required by these applications and services. Unlike prior methods, we propose a novel approach in which transformation logic is embedded into a familiar and expressive spreadsheet-like formula mapping language. Popular transformation patterns provided by transformation languages and mapping tools, that are relevant to spreadsheet-based data transformation, are supported in the language via formulas. Consequently, the language avoids cluttering the source spreadsheets with transformations and turns out to be helpful when multiple schemas are targeted. We implemented a prototype and evaluated the benefits of our approach via experiments in a real application. The experimental results confirmed the benefits of our approach.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"77 ","pages":"1749-1754"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2063576.2063829","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72430786","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}
Spoken Web is an alternative Web for low-literacy users in the developing world. People can create audio content over phone and share on the Spoken Web. This enables easy creation of locally relevant content. Even on the World Wide Web in developed regions, the recent increase in traffic is due to the locally relevant content created on social networking sites. This paper argues that content search and ranking in the new scenario needs a re-look. The generic model of using in-links for ranking such content is not an appropriate measure of the content relevance in such a collaborative Web 2.0 world. This paper aims to bring the social context in Spoken Web ranking. We formulate a relationship function between the query-creator and the content-creator and use this as one measure of the content relevance to the user. The relationship function uses the geographical location of the two people and their prior browsing preferences as parameters to determine the relationship between the two users. Further we also determine the trustability of the content based on the content creator's acceptance measure by the social network. We use these two features in addition to the term-frequency - inverse-term-frequency match to rank the search results in context of the social network of the query-creator and provide a more specific and socially relevant result to the user.
{"title":"Social ranking for spoken web search","authors":"Shrey Sahay, Nitendra Rajput, Niketan Pansare","doi":"10.1145/2063576.2063840","DOIUrl":"https://doi.org/10.1145/2063576.2063840","url":null,"abstract":"Spoken Web is an alternative Web for low-literacy users in the developing world. People can create audio content over phone and share on the Spoken Web. This enables easy creation of locally relevant content. Even on the World Wide Web in developed regions, the recent increase in traffic is due to the locally relevant content created on social networking sites. This paper argues that content search and ranking in the new scenario needs a re-look. The generic model of using in-links for ranking such content is not an appropriate measure of the content relevance in such a collaborative Web 2.0 world. This paper aims to bring the social context in Spoken Web ranking. We formulate a relationship function between the query-creator and the content-creator and use this as one measure of the content relevance to the user. The relationship function uses the geographical location of the two people and their prior browsing preferences as parameters to determine the relationship between the two users. Further we also determine the trustability of the content based on the content creator's acceptance measure by the social network. We use these two features in addition to the term-frequency - inverse-term-frequency match to rank the search results in context of the social network of the query-creator and provide a more specific and socially relevant result to the user.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"4 4 1","pages":"1835-1840"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75941534","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}
Debapriyo Majumdar, R. Catherine, S. Ikbal, Karthik Venkat Ramanan
Improving productivity of practitioners through effective knowledge management and delivering high quality service in Application Management Services (AMS) domain, are key focus areas for all IT services organizations. One source of historical knowledge in AMS is the large amount of resolved problem ticket data which are often confidential, immensely valuable, but majority of it is of very bad quality. In this paper, we present a knowledge management tool that detects the quality of information present in problem tickets and enables effective knowledge search in tickets by prioritizing quality data in the search ranking. The tool facilitates leveraging of knowledge across different AMS accounts, while preserving data privacy, by masking client confidential information. It also extracts several relevant entities contained in the noisy unstructured text entered in the tickets and presents them to the users. We present several experimental evaluations and a pilot study conducted with an AMS account which show that our tool is effective and leads to substantial improvement in productivity of the practitioners.
{"title":"Privacy protected knowledge management in services with emphasis on quality data","authors":"Debapriyo Majumdar, R. Catherine, S. Ikbal, Karthik Venkat Ramanan","doi":"10.1145/2063576.2063848","DOIUrl":"https://doi.org/10.1145/2063576.2063848","url":null,"abstract":"Improving productivity of practitioners through effective knowledge management and delivering high quality service in Application Management Services (AMS) domain, are key focus areas for all IT services organizations. One source of historical knowledge in AMS is the large amount of resolved problem ticket data which are often confidential, immensely valuable, but majority of it is of very bad quality. In this paper, we present a knowledge management tool that detects the quality of information present in problem tickets and enables effective knowledge search in tickets by prioritizing quality data in the search ranking. The tool facilitates leveraging of knowledge across different AMS accounts, while preserving data privacy, by masking client confidential information. It also extracts several relevant entities contained in the noisy unstructured text entered in the tickets and presents them to the users. We present several experimental evaluations and a pilot study conducted with an AMS account which show that our tool is effective and leads to substantial improvement in productivity of the practitioners.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"144 2","pages":"1889-1894"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2063576.2063848","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72482215","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}
Jungho Lee, Seungjae Lee, Yong-seok Seo, Won-young Yoo
The piracy of copyrighted digital content over the Internet infringes copyrights and damages the digital content industry. Accordingly, identifying and monitoring technology on the online content service like fingerprinting is getting valuable through the explosion of digital content sharing. This paper proposes a robust video fingerprinting feature to identify a modified video clip from a large scale database. Hierarchical symmetric difference feature is proposed in order to offer efficient video fingerprinting. The feature is robust and pairwise independent against various video modifications such as compression, resizing, or cropping. Moreover, videos undergoing a transformation such as flipping or mirroring can be identified by simply disordering the bit pattern of fingerprints. The performance of the proposed feature is extensively experimented on 6,482 hours of database and the experimental results show that the proposed fingerprinting is efficient and robust against various modifications.
{"title":"Robust video fingerprinting based on hierarchical symmetric difference feature","authors":"Jungho Lee, Seungjae Lee, Yong-seok Seo, Won-young Yoo","doi":"10.1145/2063576.2063897","DOIUrl":"https://doi.org/10.1145/2063576.2063897","url":null,"abstract":"The piracy of copyrighted digital content over the Internet infringes copyrights and damages the digital content industry. Accordingly, identifying and monitoring technology on the online content service like fingerprinting is getting valuable through the explosion of digital content sharing. This paper proposes a robust video fingerprinting feature to identify a modified video clip from a large scale database. Hierarchical symmetric difference feature is proposed in order to offer efficient video fingerprinting. The feature is robust and pairwise independent against various video modifications such as compression, resizing, or cropping. Moreover, videos undergoing a transformation such as flipping or mirroring can be identified by simply disordering the bit pattern of fingerprints. The performance of the proposed feature is extensively experimented on 6,482 hours of database and the experimental results show that the proposed fingerprinting is efficient and robust against various modifications.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"8 1","pages":"2089-2092"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74972888","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}
Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management
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