The Experts below are selected from a list of 88101 Experts worldwide ranked by ideXlab platform

Alberto Del Bimbo - One of the best experts on this subject based on the ideXlab platform.

  • socializing the semantic gap a Comparative Survey on image tag assignment refinement and retrieval
    ACM Computing Surveys, 2016
    Co-Authors: Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G M Snoek, Alberto Del Bimbo
    Abstract:

    Where previous reviews on content-based image retrieval emphasize what can be seen in an image to bridge the semantic gap, this Survey considers what people tag about an image. A comprehensive treatise of three closely linked problems (i.e., image tag assignment, refinement, and tag-based image retrieval) is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, that is, estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this article introduces a two-dimensional taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison with the state of the art, a new experimental protocol is presented, with training sets containing 10,000, 100,000, and 1 million images, and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the Survey aims to provide an overview of the past and foster progress for the near future.

  • socializing the semantic gap a Comparative Survey on image tag assignment refinement and retrieval
    arXiv: Information Retrieval, 2015
    Co-Authors: Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G M Snoek, Alberto Del Bimbo
    Abstract:

    Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this Survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the Survey aims to provide an overview of the past and foster progress for the near future.

Tiberio Uricchio - One of the best experts on this subject based on the ideXlab platform.

  • socializing the semantic gap a Comparative Survey on image tag assignment refinement and retrieval
    ACM Computing Surveys, 2016
    Co-Authors: Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G M Snoek, Alberto Del Bimbo
    Abstract:

    Where previous reviews on content-based image retrieval emphasize what can be seen in an image to bridge the semantic gap, this Survey considers what people tag about an image. A comprehensive treatise of three closely linked problems (i.e., image tag assignment, refinement, and tag-based image retrieval) is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, that is, estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this article introduces a two-dimensional taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison with the state of the art, a new experimental protocol is presented, with training sets containing 10,000, 100,000, and 1 million images, and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the Survey aims to provide an overview of the past and foster progress for the near future.

  • socializing the semantic gap a Comparative Survey on image tag assignment refinement and retrieval
    arXiv: Information Retrieval, 2015
    Co-Authors: Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G M Snoek, Alberto Del Bimbo
    Abstract:

    Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this Survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the Survey aims to provide an overview of the past and foster progress for the near future.

Gunther Gebhardt - One of the best experts on this subject based on the ideXlab platform.

  • derivatives usage in risk management by us and german non financial firms a Comparative Survey
    Journal of International Financial Management and Accounting, 1999
    Co-Authors: Gordon M Bodnar, Gunther Gebhardt
    Abstract:

    This paper is a Comparative study of the responses to the 1995 Wharton School Survey of derivative usage among US non-financial firms and a 1997 companion Survey on German non-financial firms. It is not a mere comparison of the results of both studies but a Comparative study, drawing a comparable subsample of firms from the US study to match the sample of German firms on both size and industry composition. We find that German firms are more likely to use derivatives than US firms, with 78 percent of German firms using derivatives compared to 57 percent of US firms. Aside from this higher overall usage, the general pattern of usage across industry and size groupings is comparable across the two countries. In both countries, foreign currency derivative usage is most common, followed closely by interest rate derivatives, with commodity derivatives a distant third. In contrast to the similarities, firms in the two countries differ notably on issues such as the primary goal of hedging, their choice of instruments, and the influence of their market view when taking derivative positions. These differences appear to be driven by the greater importance of financial accounting statements in Germany than the US and stricter German corporate policies of control over derivative activities within the firm.

  • derivatives usage in risk management by u s and german non financial firms a Comparative Survey
    Research Papers in Economics, 1998
    Co-Authors: Gordon M Bodnar, Gunther Gebhardt
    Abstract:

    This paper is a Comparative study of the responses to the 1995 Wharton School Survey of derivative usage among US non-financial firms and a 1997 companion Survey on German non-financial firms. It is not a mere comparison of the results of both studies but a Comparative study, drawing a comparable subsample of firms from the US study to match the sample of German firms on both size and industry composition. We find that German firms are more likely to use derivatives than US firms, with 78% of German firms using derivatives compared to 57% of US firms. Aside from this higher overall usage, the general pattern of usage across industry and size groupings is comparable across the two countries. In both countries, foreign currency derivative usage is most common, followed closely by interest rate derivatives, with commodity derivatives a distant third. Usage rates across all three classes of derivatives are higher for German firms than US firms. In contrast to the similarities, firms in the two countries differ notably on issues such as the primary goal of hedging, their choice of instruments, and the influence of their market view when taking derivative positions. These differences appear to be driven by the greater importance of financial accounting statements in Germany than the US and stricter German corporate policies of control over derivative activities within the firm. German firms also indicate significantly less concern about derivative related issues than US firms, which appears to arise from a more basic and simple strategy for using derivatives. Finally, among the derivative non-users, German firms tend to cite reasons suggesting derivatives were not needed whereas US firms tend to cite reasons suggesting a possible role for derivatives, but a hesitation to use them for some reason.

  • derivatives usage in risk management by us and german non financial firms a Comparative Survey
    National Bureau of Economic Research, 1998
    Co-Authors: Gordon M Bodnar, Gunther Gebhardt
    Abstract:

    This paper is a Comparative study of the responses to the 1995 Wharton School Survey of derivative usage among US non-financial firms and a 1997 companion Survey on German non-financial firms. It is not a mere comparison of the results of both studies, but a Comparative study, drawing a comparable subsample of firms from the US study to match the sample of German firms on both size and industry composition. We find that German firms are more likely to use derivatives than US firms, with 78% of German firms using derivatives compared to 57% of US firms. Aside from this higher overall usage, the general pattern of usage across industry and size groupings is comparable across the two countries. In both countries, foreign currency derivative usage is most common, followed closely by interest rate derivatives, with commodity derivatives a distant third. In contrast to the similarities, firms in the two countries differ notably on issues such as the primary goal of hedging, their choice of instruments, and the influence of their market view when taking derivative positions. These differences appear to be driven by the greater importance of financial accounting statements in Germany than the US and stricter German corporate policies of control over derivative activities within the firm.

Marco Bertini - One of the best experts on this subject based on the ideXlab platform.

  • socializing the semantic gap a Comparative Survey on image tag assignment refinement and retrieval
    ACM Computing Surveys, 2016
    Co-Authors: Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G M Snoek, Alberto Del Bimbo
    Abstract:

    Where previous reviews on content-based image retrieval emphasize what can be seen in an image to bridge the semantic gap, this Survey considers what people tag about an image. A comprehensive treatise of three closely linked problems (i.e., image tag assignment, refinement, and tag-based image retrieval) is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, that is, estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this article introduces a two-dimensional taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison with the state of the art, a new experimental protocol is presented, with training sets containing 10,000, 100,000, and 1 million images, and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the Survey aims to provide an overview of the past and foster progress for the near future.

  • socializing the semantic gap a Comparative Survey on image tag assignment refinement and retrieval
    arXiv: Information Retrieval, 2015
    Co-Authors: Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G M Snoek, Alberto Del Bimbo
    Abstract:

    Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this Survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the Survey aims to provide an overview of the past and foster progress for the near future.

Lamberto Ballan - One of the best experts on this subject based on the ideXlab platform.

  • socializing the semantic gap a Comparative Survey on image tag assignment refinement and retrieval
    ACM Computing Surveys, 2016
    Co-Authors: Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G M Snoek, Alberto Del Bimbo
    Abstract:

    Where previous reviews on content-based image retrieval emphasize what can be seen in an image to bridge the semantic gap, this Survey considers what people tag about an image. A comprehensive treatise of three closely linked problems (i.e., image tag assignment, refinement, and tag-based image retrieval) is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, that is, estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this article introduces a two-dimensional taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison with the state of the art, a new experimental protocol is presented, with training sets containing 10,000, 100,000, and 1 million images, and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the Survey aims to provide an overview of the past and foster progress for the near future.

  • socializing the semantic gap a Comparative Survey on image tag assignment refinement and retrieval
    arXiv: Information Retrieval, 2015
    Co-Authors: Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G M Snoek, Alberto Del Bimbo
    Abstract:

    Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this Survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the Survey aims to provide an overview of the past and foster progress for the near future.