Thick 2D relations for document understanding

Aiello, Marco; Smeulders, Arnold M.W.

doi:10.1016/j.ins.2003.05.015

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Structural representations stand as a kind of alternative approach with some preliminary results, but to be further investigated. Graph-Based Representation of an image is a rich domain, relations between regions or points of interest can be modeled in many ways, among them, we can cite the representations issued from Delaunay triangulation [84], Allen algebra [83] or a neighboring graph.…”

Section: Analysis and Discussionmentioning

confidence: 99%

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Structured representations in a content based image retrieval context

Raveaux

Burie

Ogier

2013

Journal of Visual Communication and Image Representation

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International audienceHere, we propose an automatic system to annotate and retrieve images. We assume that regions in an image can be described using a vocabulary of blobs. Blobs are generated from image features using clustering. Features are locally extracted on regions to capture Color, Texture and Shape information. Regions are processed by an efficient segmentation algorithm. Images are structured into a region adjacency graph to consider spatial relationships between regions. This representation is used to perform a similarity search into an image set. Hence, the user can express his need by giving a query image, and thereafter receiving as a result all similar images. Our graph based approach is benchmarked to conventional Bag of Words methods. Results tend to reveal a good behavior in classification of our graph based solution on two publicly available databases. Experiments illustrate that a structural approach requires a smaller vocabulary size to reach its best performance

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Section: Analysis and Discussionmentioning

confidence: 99%

“…We can mention GBR methods using Bi-dimensional Allen Algebra (Ref. [83]) or Delaunay triangulation (Ref. [84]).…”

Section: Recognition Rate Comparison Gbr Vs Bowmentioning

confidence: 99%

Structured representations in a content based image retrieval context

Raveaux

Burie

Ogier

2013

Journal of Visual Communication and Image Representation

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“…The output of the spatial reasoner is a (cyclic) graph where edges represent instances of the partial ordering relation BeforeInReading. A reading order is then defined as a full path in this graph, and is determined by means of an extension of a standard topological sort [12]. Due to the generality of the document encoding rule used by the spatial reasoner, it is likely that one obtains more than one reading order, especially for complex documents with many blocks.…”

Section: Background and Related Workmentioning

confidence: 99%

Machine Learning for Reading Order Detection in Document Image Understanding

Malerba

Ceci

Berardi

2008

Machine Learning in Document Analysis and Recognition

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Summary. Document image understanding refers to logical and semantic analysis of document images in order to extract information understandable to humans and codify it into machine-readable form. Most of the studies on document image understanding have targeted the specific problem of associating layout components with logical labels, while less attention has been paid to the problem of extracting relationships between logical components, such as cross-references. In this chapter, we investigate the problem of detecting the reading order relationship between components of a logical structure. The domain specific knowledge required for this task is automatically acquired from a set of training examples by applying a machine learning method. The input of the learning method is the description of "chains" of layout components defined by the user. The output is a logical theory which defines two predicates, f irst to read/1 and succ in reading/2, useful for consistently reconstructing all chains in the training set. Only spatial information on the page layout is exploited for both single and multiple chain reconstruction. The proposed approach has been evaluated on a set of document images processed by the system WISDOM++.

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“…In [Aiello et al, 2002] we presented a system which, by using spatial reasoning [Aiello and Smeulders, 2004] and text processing techniques [Sparck Jones, 1972, Salton and McGill, 1983, Baeza-Yates and Ribeiro-Neto, 1999, is able to extract the reading order from document images for documents having very different layouts. In particular, a spatial language for rectangles [Balbiani et al, 1998], based on the work on interval relations of Allen [Allen, 1983], is used to describe both the elements in a document and general rules to analyze them.…”

Section: Three Algorithms For Article Clusteringmentioning

confidence: 99%

Textual Article Clustering in Newspaper Pages

Aiello

Pegoretti

2006

Applied Artificial Intelligence

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In the analysis of a newspaper page an important step is the clustering of various text blocks into logical units, i.e., into articles. We propose three algorithms based on text processing techniques to cluster articles in newspaper pages. Based on the complexity of the three algorithms and experiment on actual pages from the Italian newspaper L'Adige, we select one of the algorithms as the preferred choice to solve the textual clustering problem.

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Thick 2D relations for document understanding

Cited by 6 publications

References 24 publications

Structured representations in a content based image retrieval context

Structured representations in a content based image retrieval context

Machine Learning for Reading Order Detection in Document Image Understanding

Textual Article Clustering in Newspaper Pages

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