The Experts below are selected from a list of 26613 Experts worldwide ranked by ideXlab platform
Ferran Marques - One of the best experts on this subject based on the ideXlab platform.
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MUSCLE - Rich internet application for semi-Automatic Annotation of semantic shots on keyframes
Lecture Notes in Computer Science, 2012Co-Authors: Elisabet Carcel, Manuel Martos, Xavier Giro-i-nieto, Ferran MarquesAbstract:This paper describes a system developed for the semi- Automatic Annotation of keyframes in a broadcasting company. The tool aims at assisting archivists who traditionally label every keyframe manually by suggesting them an Automatic Annotation that they can intuitively edit and validate. The system is valid for any domain as it uses generic MPEG-7 visual descriptors and binary SVM classifiers. The classification engine has been tested on the multiclass problem of semantic shot detection, a type of metadata used in the company to index new content ingested in the system. The detection performance has been tested in two different domains: soccer and parliament. The core engine is accessed by a Rich Internet Application via a web service. The graphical user interface allows the edition of the suggested labels with an intuitive drag and drop mechanism between rows of thumbnails, each row representing a different semantic shot class. The system has been described as complete and easy to use by the professional archivists at the company.
Elisabet Carcel - One of the best experts on this subject based on the ideXlab platform.
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MUSCLE - Rich internet application for semi-Automatic Annotation of semantic shots on keyframes
Lecture Notes in Computer Science, 2012Co-Authors: Elisabet Carcel, Manuel Martos, Xavier Giro-i-nieto, Ferran MarquesAbstract:This paper describes a system developed for the semi- Automatic Annotation of keyframes in a broadcasting company. The tool aims at assisting archivists who traditionally label every keyframe manually by suggesting them an Automatic Annotation that they can intuitively edit and validate. The system is valid for any domain as it uses generic MPEG-7 visual descriptors and binary SVM classifiers. The classification engine has been tested on the multiclass problem of semantic shot detection, a type of metadata used in the company to index new content ingested in the system. The detection performance has been tested in two different domains: soccer and parliament. The core engine is accessed by a Rich Internet Application via a web service. The graphical user interface allows the edition of the suggested labels with an intuitive drag and drop mechanism between rows of thumbnails, each row representing a different semantic shot class. The system has been described as complete and easy to use by the professional archivists at the company.
Marco Bertini - One of the best experts on this subject based on the ideXlab platform.
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ACM Multimedia - Automatic Annotation and semantic retrieval of video sequences using multimedia ontologies
Proceedings of the 14th annual ACM international conference on Multimedia - MULTIMEDIA '06, 2006Co-Authors: Marco Bertini, Alberto Del Bimbo, Carlo TorniaiAbstract:Effective usage of multimedia digital libraries has to deal with the problem of building efficient content Annotation and retrieval tools. MOM (Multimedia Ontology Manager) is a complete system that allows the creation of multimedia ontologies, supports Automatic Annotation and creation of extended text (and audio) commentaries of video sequences, and permits complex queries by reasoning on the ontology.
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ACM Multimedia - MOM: multimedia ontology manager. A framework for Automatic Annotation and semantic retrieval of video sequences
Proceedings of the 14th annual ACM international conference on Multimedia - MULTIMEDIA '06, 2006Co-Authors: Marco Bertini, Alberto Del Bimbo, Rita Cucchiara, Carlo Torniai, Costantino GranaAbstract:Effective usage of multimedia digital libraries has to deal with the problem of building efficient content Annotation and retrieval tools. MOM (Multimedia Ontology Manager) is a complete system that allows the creation of multimedia ontologies, supports Automatic Annotation and creation of extended text (and audio) commentaries of video sequences, and permits complex queries by reasoning on the ontology.
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CIARP - Automatic Annotation of sport video content
Lecture Notes in Computer Science, 2005Co-Authors: Marco Bertini, Alberto Del Bimbo, W. NunziatiAbstract:Automatic semantic Annotation of video streams allows to extract significant clips for archiving and retrieval of video content. In this paper, we present a system that performs Automatic Annotation of soccer videos, detecting principal highlights, and recognizing identity of players. Highlight detection is carried out by means of finite state machines that encode domain knowledge, while player identification is based on face detection, and on the analysis of contextual information such as jersey’s numbers and superimposed text captions. Results obtained on actual soccer videos shows overall highlight detection rates of about 90%. Lower, but still promising, accuracy is achieved on the very difficult player identification task.
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Multimedia Information Retrieval - Semantic video adaptation based on Automatic Annotation of sport videos
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval - MIR '04, 2004Co-Authors: Marco Bertini, Alberto Del Bimbo, Rita Cucchiara, Andrea PratiAbstract:Semantic video adaptation improves traditional adaptation by taking into account the degree of relevance of the different portions of the content. It employs solutions to detect the significant parts of the video and applies different compression ratios to elements that have different importance. Performance of semantic adaptation heavily depends on the precision of the Automatic Annotation and the way of operation of the codec which is used to perform adaptation at the event or object level. In this paper, we discuss critical factors that affect performance of Automatic Annotation and define new performance measures of semantic adaptation, Viewing Quality Loss and Bitrate Cost Increase, that are obtained from classical PSNR and Bit Rate, but relate the results of semantic adaptation with the user's preferences and expectations. The new measures are discussed in detail for a system of sport Annotation and adaptation with reference to different user profiles
Douglas Eck - One of the best experts on this subject based on the ideXlab platform.
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temporal pooling and multiscale learning for Automatic Annotation and ranking of music audio
International Symposium Conference on Music Information Retrieval, 2011Co-Authors: Philippe Hamel, Simon Lemieux, Yoshua Bengio, Douglas EckAbstract:This paper analyzes some of the challenges in performing Automatic Annotation and ranking of music audio, and proposes a few improvements. First, we motivate the use of principal component analysis on the mel-scaled spectrum. Secondly, we present an analysis of the impact of the selection of pooling functions for summarization of the features over time. We show that combining several pooling functions improves the performance of the system. Finally, we introduce the idea of multiscale learning. By incorporating these ideas in our model, we obtained state-of-the-art performance on the Magnatagatune dataset.
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ISMIR - Temporal pooling and multiscale learning for Automatic Annotation and ranking of music audio
2011Co-Authors: Philippe Hamel, Simon Lemieux, Yoshua Bengio, Douglas EckAbstract:This paper analyzes some of the challenges in performing Automatic Annotation and ranking of music audio, and proposes a few improvements. First, we motivate the use of principal component analysis on the mel-scaled spectrum. Secondly, we present an analysis of the impact of the selection of pooling functions for summarization of the features over time. We show that combining several pooling functions improves the performance of the system. Finally, we introduce the idea of multiscale learning. By incorporating these ideas in our model, we obtained state-of-the-art performance on the Magnatagatune dataset.
Manuel Martos - One of the best experts on this subject based on the ideXlab platform.
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MUSCLE - Rich internet application for semi-Automatic Annotation of semantic shots on keyframes
Lecture Notes in Computer Science, 2012Co-Authors: Elisabet Carcel, Manuel Martos, Xavier Giro-i-nieto, Ferran MarquesAbstract:This paper describes a system developed for the semi- Automatic Annotation of keyframes in a broadcasting company. The tool aims at assisting archivists who traditionally label every keyframe manually by suggesting them an Automatic Annotation that they can intuitively edit and validate. The system is valid for any domain as it uses generic MPEG-7 visual descriptors and binary SVM classifiers. The classification engine has been tested on the multiclass problem of semantic shot detection, a type of metadata used in the company to index new content ingested in the system. The detection performance has been tested in two different domains: soccer and parliament. The core engine is accessed by a Rich Internet Application via a web service. The graphical user interface allows the edition of the suggested labels with an intuitive drag and drop mechanism between rows of thumbnails, each row representing a different semantic shot class. The system has been described as complete and easy to use by the professional archivists at the company.
