Proceedings of the fifth annual workshop on Computational learning theory

A novel method for image retrieval based on structure elements' descriptor

A survey of document image word spotting techniques

This paper presents an overview of the ImageCLEF 2015 evaluation campaign, an event that was organized as part of the CLEF labs 2015 ImageCLEF is an ongoing initiative that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to databases of images in various usage scenarios and domains In 2015, the 13th edition of ImageCLEF, four main tasks were proposed: 1 automatic concept annotation, localization and sentence description generation for general images; 2 identification, multi-label classification and separation of compound figures from biomedical literature; 3 clustering of x-rays from all over the body; and 4 prediction of missing radiological annotations in reports of liver CT images The x-ray task was the only fully novel task this year, although the other three tasks introduced modifications to keep up relevancy of the proposed challenges The participation was considerably positive in this edition of the lab, receiving almost twice the number of submitted working notes papers as compared to previous years

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General Overview of ImageCLEF at the CLEF 2015 Labs

Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting

http://orpheus.ee.duth.gr/download/papers/journals/A%20Document%20Image%20Retrieval%20System.pdf

A Document Image Retrieval System

http://orpheus.ee.duth.gr/download/papers/journals/Image%20Retrieval%20Systems%20Based%20On%20Compact%20Shape%20Descriptor%20and%20Relevance%20Feedback%20Information.pdf

Image retrieval systems based on compact shape descriptor and relevance feedback information

The state-of-the-art writer identification systems use a variety of different features and techniques in order to identify the writer of the handwritten text. In this paper several statistical and model based features are presented. Specifically, an improvement of a statistical feature, the edge hinge distribution, is attempted. Furthermore, the combination of this feature with a model-based feature is explored, that is based on a codebook of graphemes. For the evaluation, the Fire maker DB was used, which consists of 250 writers, including 4 pages per writer. The best result for the statistical suggested approach, the skeleton hinge distribution, achieved accuracy of 90.8%, while the combination of this method with the codebook of graphemes reached 96%.

Writer Identification Using a Statistical and Model Based Approach

A system was developed able to retrieve specific documents from a document collection. In this system the query is given in text by the user and then transformed into image. Appropriate features were in order to capture the general shape of the query, and ignore details due to noise or different fonts. In order to demonstrate the effectiveness of our system, we used a collection of noisy documents and we compared our results with those of a commercial OCR package.

Kavallieratou Ergina

Papers

A Document Image Retrieval System

Image retrieval systems based on compact shape descriptor and relevance feedback information

Writer Identification Using a Statistical and Model Based Approach

Developing document image retrieval system

Biofouling Detection in Fish Farming Nets Using Neural Networks