Structure Analysis

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The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

Tomoko Izumi - One of the best experts on this subject based on the ideXlab platform.

  • discriminative approach to predicate argument Structure Analysis with zero anaphora resolution
    Meeting of the Association for Computational Linguistics, 2009
    Co-Authors: Kenji Imamura, Kuniko Saito, Tomoko Izumi
    Abstract:

    This paper presents a predicate-argument Structure Analysis that simultaneously conducts zero-anaphora resolution. By adding noun phrases as candidate arguments that are not only in the sentence of the target predicate but also outside of the sentence, our analyzer identifies arguments regardless of whether they appear in the sentence or not. Because we adopt discriminative models based on maximum entropy for argument identification, we can easily add new features. We add language model scores as well as contextual features. We also use contextual information to restrict candidate arguments.

Yoon-chul Choy - One of the best experts on this subject based on the ideXlab platform.

  • Logical Structure Analysis and generation for Structured documents: a syntactic approach
    IEEE Transactions on Knowledge and Data Engineering, 2003
    Co-Authors: Yoon-chul Choy
    Abstract:

    This paper presents a syntactic method for sophisticated logical Structure Analysis that transforms document images with multiple pages and hierarchical Structure into an electronic document based on SGML/XML. To produce a logical Structure more accurately and quickly than previous works of which the basic units are text lines, the proposed parsing method takes text regions with hierarchical Structure as input. Furthermore, we define a document model that is able to describe geometric characteristics and logical Structure information of documents efficiently and present its automated creation method. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) show that the method has performed logical Structure Analysis successfully and generated a document model automatically. Particularly, the method generates SGML/XML documents as the result of structural Analysis, so that it enhances the reusability of documents and independence of platform.

  • Geometric Structure Analysis of document images: a knowledge-based approach
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
    Co-Authors: Yoon-chul Choy
    Abstract:

    This paper presents a knowledge-based method for sophisticated geometric Structure Analysis of technical journal pages. The proposed knowledge base encodes geometric characteristics that are not only common in technical journals but also publication-specific in the form of rules. The method takes the hybrid of top-down and bottom-up techniques and consists of two phases: region segmentation and identification. Generally, the result of the segmentation process does not have a one-to-one matching with composite layout components. Therefore, the proposed method identifies non-text objects, such as images, drawings, and tables, as well as text objects, by splitting or grouping segmented regions into composite layout components. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence show that the proposed method has performed geometric Structure Analysis successfully on more than 99 percent of the test images.

Changsheng Xu - One of the best experts on this subject based on the ideXlab platform.

  • Automatic music summarization based on music Structure Analysis
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005
    Co-Authors: Xi Shao, N.c. Maddage, Changsheng Xu
    Abstract:

    In this paper, we present a novel approach for music summarization based on music Structure Analysis. From the audio signal, we first extract the note onset representing the time tempo of the song and the music Structure Analysis can be performed based on this tempo information. After music content has been Structured into different semantic regions such as introduction (intro), verse, chorus, ending (outro), etc., the final music summary can be created with chorus and music phrases which are included anterior or posterior to the selected chorus to get the desired length of the final summary. In this way, we can guarantee that the summaries begin and end at meaningful music phrase boundaries, which is a difficult problem for existing music summarization methods. Experiments show our proposed method can capture the main theme of the music compared to the ideal summaries selected by music experts and user subjective evaluation indicates our proposed method has a good performance.

  • Soccer replay detection using scene transition Structure Analysis
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005
    Co-Authors: Jinjun Wang, Engsiong Chng, Changsheng Xu
    Abstract:

    Replay scene detection is a useful technique for content based sports video Analysis. Most current researchers try to find suitable visual and/or compressed domain features to detect the replay scene from a broadcast video. We present a novel approach using context information from the concurrence of replay and other types of shots to detect the replay scenes. We first perform a shot classification and then a scene transition Structure Analysis on the generated shot label sequence to extract the replay scene. The proposed model is computationally fast and some promising results were obtained.

Kenji Imamura - One of the best experts on this subject based on the ideXlab platform.

  • discriminative approach to predicate argument Structure Analysis with zero anaphora resolution
    Meeting of the Association for Computational Linguistics, 2009
    Co-Authors: Kenji Imamura, Kuniko Saito, Tomoko Izumi
    Abstract:

    This paper presents a predicate-argument Structure Analysis that simultaneously conducts zero-anaphora resolution. By adding noun phrases as candidate arguments that are not only in the sentence of the target predicate but also outside of the sentence, our analyzer identifies arguments regardless of whether they appear in the sentence or not. Because we adopt discriminative models based on maximum entropy for argument identification, we can easily add new features. We add language model scores as well as contextual features. We also use contextual information to restrict candidate arguments.

I. King - One of the best experts on this subject based on the ideXlab platform.

  • Video summarization by video Structure Analysis and graph optimization
    2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004
    Co-Authors: Shi Lu, I. King
    Abstract:

    We propose a novel video summarization method that combines video Structure Analysis and graph optimization. First, we analyze the Structure of the video, find the boundaries of video scenes, then we calculate each scene's skimming length based on its Structure and content entropy. Second, we define a spatial-temporal dissimilarity function between video shots and model each video scene as a graph, then find each scene's optimal skimming in the graph with dynamic programming. Finally, the whole video's skimming is obtained by concatenating the skimmings of the scenes. Experimental results show that our approach preserves the scene level Structure and ensures balanced coverage of the major contents of the original video.