Structured multimedia document classification

Denoyer, Ludovic; Vittaut, Jean-Noël; Gallinari, Patrick; Brunessaux, Stéphan

doi:10.1145/958220.958249

Cited by 15 publications

(7 citation statements)

References 16 publications

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“…Some of the methods utilize relevant text information to enhance image features [7]. In [8], the authors introduce an approach that combines the textual and visual statistics as a single feature vector. The approach applies color histograms and a dominant orientation histogram to represent the images into vectors, then combines the vectors with a text vector generated from Latent Semantic Indexing.…”

Section: B Multi-model Documentmentioning

confidence: 99%

A Self-Relevant CNN-SVM Model for Problem Classification in K-12 Question-Driven Learning

Chen

Liu

et al. 2020

IEEE Access

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With the development and progress of science and technology, the learning patterns also evolve. In Question-Driven learning, students clarify and validate what they learn by answering questions. Such a large number of questions needs good management. A well-performed management can avoid the situation that learning materials with the same knowledge set are defined into different sections due to ambiguous expressions. In this work, we propose a hybrid classification model using the CNN-SVM that focuses on K-12 learning materials. We combine the Word2Vec feature and the hidden layer feature of CNN. In response to a current question that contains text and image, we also introduce a multi-modal preprocessing approach. The experiment shows that our preprocessing method and the hybrid model can outperform the two state-of-the-art methods.

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Section: B Multi-model Documentmentioning

confidence: 99%

A Self-Relevant CNN-SVM Model for Problem Classification in K-12 Question-Driven Learning

Chen

Liu

et al. 2020

IEEE Access

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“…Denoyer [8,9] propose a statistical graph representation for the classification of structured documents. Their system classifies multimedia documents, where text and images are mixed together in HTML pages.…”

Section: Related Workmentioning

confidence: 99%

“…where l β corresponds to the model of the news category l w from the set (9) of L categories. In this setting, we define a collection…”

Section: Web News Categorizationmentioning

confidence: 99%

Web news categorization using a cross-media document graph

Iria

Ciravegna

Magalhães

2009

Proceedings of the ACM International Conference on Image and Video Retrieval

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In this paper we propose a multimedia categorization framework that is able to exploit information across different parts of a multimedia document (e.g., a Web page, a PDF, a Microsoft Office document). For example, a Web news page is composed by text describing some event (e.g., a car accident) and a picture containing additional information regarding the real extent of the event (e.g., how damaged the car is) or providing evidence corroborating the text part. The framework handles multimedia information by considering not only the document's text and images data but also the layout structure which determines how a given text block is related to a particular image. The novelties and contributions of the proposed framework are: (1) support of heterogeneous types of multimedia documents; (2) a documentgraph representation method; and (3) the computation of crossmedia correlations. Moreover, we applied the framework to the tasks of categorising Web news feed data, and our results show a significant improvement over a single-medium based framework.

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“…Moreover, its learning complexity is linear with respect to the size of the documents. This type of model has been used both for the categorization and the clustering of XML documents and the authors have proposed extensions that take into account different information content (text, pictures,...) for the multimedia filtering task (Denoyer et al, 2003). A discriminative algorithm has also been developed for the categorization task.…”

Section: Stochastic Generative Modelmentioning

confidence: 99%

Mining XML Documents

Candillier¹,

Denoyer²,

Gallinari³

et al. 2008

Data Mining Patterns

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XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the increasing size of XML collections as information sources, mining techniques that traditionally exist for text collections or databases need to be adapted and new methods to be invented to exploit the particular structure of XML documents. Basically XML documents can be seen as trees, which are well known to be complex structures. This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. We focus on three mining tasks: classification and clustering which are standard for text collections; discovering of frequent tree structure which is especially important for heterogeneous collection. This chapter presents some recent approaches and algorithms to support these tasks together with experimental evaluation on a variety of large XML collections

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Structured multimedia document classification

Cited by 15 publications

References 16 publications

A Self-Relevant CNN-SVM Model for Problem Classification in K-12 Question-Driven Learning

A Self-Relevant CNN-SVM Model for Problem Classification in K-12 Question-Driven Learning

Web news categorization using a cross-media document graph

Mining XML Documents

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