The Experts below are selected from a list of 138 Experts worldwide ranked by ideXlab platform

Azzurra Ragone - One of the best experts on this subject based on the ideXlab platform.

  • web 3 0 in Action Vector space model for semantic movie recommendations
    ACM Symposium on Applied Computing, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    Abstract:

    In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

  • SAC - Web 3.0 in Action: Vector Space Model for semantic (movie) Recommendations
    Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    Abstract:

    In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

Roberto Mirizzi - One of the best experts on this subject based on the ideXlab platform.

  • web 3 0 in Action Vector space model for semantic movie recommendations
    ACM Symposium on Applied Computing, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    Abstract:

    In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

  • SAC - Web 3.0 in Action: Vector Space Model for semantic (movie) Recommendations
    Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    Abstract:

    In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

Eugenio Di Sciascio - One of the best experts on this subject based on the ideXlab platform.

  • web 3 0 in Action Vector space model for semantic movie recommendations
    ACM Symposium on Applied Computing, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    Abstract:

    In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

  • SAC - Web 3.0 in Action: Vector Space Model for semantic (movie) Recommendations
    Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    Abstract:

    In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

Tommaso Di Noia - One of the best experts on this subject based on the ideXlab platform.

  • web 3 0 in Action Vector space model for semantic movie recommendations
    ACM Symposium on Applied Computing, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    Abstract:

    In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

  • SAC - Web 3.0 in Action: Vector Space Model for semantic (movie) Recommendations
    Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    Abstract:

    In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.

C. Krishna Mohan - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Action-Vectors: Unsupervised movement modeling for Action recognition
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Debaditya Roy, K. Sri Rama Murty, C. Krishna Mohan
    Abstract:

    Representation and modelling of movements play a significant role in recognising Actions in unconstrained videos. However, explicit segmentation and labelling of movements are non-trivial because of the variability associated with actors, camera viewpoints, duration etc. Therefore, we propose to train a GMM with a large number of components termed as a universal movement model (UMM). This UMM is trained using motion boundary histograms (MBH) which capture the motion trajectories associated with the movements across all possible Actions. For a particular Action video, the MAP adapted mean Vectors of the UMM are concatenated to form a fixed dimensional representation referred to as “super movement Vector” (SMV). However, SMV is still high dimensional and hence, Baum-Welch statistics extracted from the UMM are used to arrive at a compact representation for each Action video, which we refer to as an “Action-Vector”. It is shown that even without the use of class labels, Action-Vectors provide a more discriminatory representation of Action classes translating to a 8 % relative improvement in classification accuracy for Action-Vectors based on MBH features over naive MBH features on the UCF101 dataset. Furthermore, Action-Vectors projected with LDA achieve 93% accuracy on the UCF101 dataset which rivals state-of-the-art deep learning techniques.