Anca Ion

Question generation for learning evaluation

In the last decade the electronic learning became a very useful tool in the studentspsilas education from different activity domains. The accomplished studies indicated that the students substantially appreciate the e-learning method, due to the facilities: the facile information access, a better storage of the didactic material, the curricula harmonization between universities, personalized instruction. The paper presents a software tool that can be used in the e-learning process in order to automatically generate questions from course materials, based on a series of tags defined by the professor. The test creator tool permits generation of questions based on electronic materials that students have. The solution implies teachers to have a series of tags and templates that they manage. These tags are used to generate question automatically.

Semantic Based Image Retrieval Using Relevance Feedback

In this paper, we propose a method for image categorization and retrieval, by integrating knowledge from low-level and semantic features extracted from images. The low -level descriptors, like color, position, dimension and texture are extracted from each image region. These mathematical descriptors are automatically associated with intermediate semantic descriptors. The intermediate descriptors are used also for image categorization and for qualitative definition of semantic keywords in the user queries. For improving the initial query results, we apply a relevance feedback mechanism that uses the low -level descriptors of the images selected as relevant by user for producing the final query results. A support vector machine classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier and the semantic indexing, we implement a software system that can retrieve more images relevant to the query in the database efficiently.

Topic Map for Medical E-Learning

The paper presents original ways of using a modern concept - topic map - in medical e-learning. The topic map is mainly used for visualizing a thesaurus containing medical terms. The topic map is built and populated in an original manner, mapping an xml file that can be downloaded free, to an xtm file that contains the structure of the topic map. Only a part of the MeSH thesaurus was used, namely the part that includes the medical diagnosis’s names. The student can navigate through topic map depending on its interest subject, having in this way big advantages. The paper presents also how to use the topic map for semantic querying of a multimedia database with medical information and images. For retrieving the interest information this access path can be combined with another modern solution: the content-based visual query on the multimedia medical database. Combining these possibilities to access a database with medical data and images, allows students to see images and associated information in a simple and direct manner. The students are stimulated to learn, by comparing similar cases or by comparing cases that are visually similar, but with different diagnoses.

A Spatial Watermarking Algorithm for Video Images

The lack of control inherent to digital content has been put on the spotlight by copyright infringement coupled with massive content online distribution (e.g., Peer-to-Peer). Digital Rights Management seems to be the solution to counter this problem advocating the use of cryptography and other related security mechanisms to protect digital content and to associate rights with it which determine how, when and by whom it can be consumed. The rapid growth of digital multimedia technologies brings tremendous attention to the field of digital watermarking. Watermarking embeds a secret message into a cover multimedia data. In media watermarking the secret is usually a copyright notice and the cover a digital image. In digital watermarking, robustness is still a challenging problem if different sets of attacks needed to be tolerated simultaneously. In this paper we present an original spatial watermarking technique for video and images. Our approach modifies blocks of the image or frames by a spatial watermark insertion. Spatial mask of suitable size is used to hide data with less visual impairments. Watermark insertion process exploits average color of the homogeneity regions of the cover image. We took a frame-based approach to video watermarking. From video we extract a certain number of key-frames: the first, the middle, and last key-frame. The first step is decoding: transformation of mpeg to jpeg sequences, after that we select three frames that will be process by applying the watermark mask. In the reverse process of encoding we take the marked frames.

Automation of the medical diagnosis process using semantic image interpretation

This paper is a part of a complex study of developing methods for semantic interpretation of medical images, to permit the semi-automatic diagnosis. The first objective of the study is to develop new methods for medical image segmentation and a set of visual features. The second objective consists of developing a unifying framework for semantic images annotation, to be used in the process of medical diagnosis. The developed diagnosis method is based on on semantic pattern rules capable to discover associations between visual features of medical images and their diagnoses. Although we present the results achieved in endoscopic images analysis, our methods can be used to analyze other types of medical images. The prototype system was applied to real datasets and the results show high accuracy.

Mapping Image Low-Level Descriptors to Semantic Concepts

Our goal is to organize the image contents semantically. In this paper, we propose a method to classify the images semantically, using the C-fuzzy algorithm to segment the natural scenes into perceptually uniform regions. The low-level characteristics that are taken into account are: color, texture, shape, absolute spatial arrangement, spatial coherency, and dimension. Since humans are the ultimate users of most image retrieval systems, it is important to organize the contents semantically, according to meaningful categories. This requires an understanding of the important semantic categories that humans use for image classification, and the extraction of meaningful image features that can discriminate between these categories. A lot of experiments, in which the human subjects had to group images into semantic categories and to explain the criteria for their choice, were realized. From these experiments, we identify the semantic categories (landscapes, animals, flowers, etc), the semantic indicators or intermediate descriptors and their visual characteristics.

Image Annotation by Learning Rules from Regions Patterns

The modeling of multimedia and especially the semantic gap between the visual features and semantic concepts become an important domain due to the quantity of visual digital content, which speedily grows. In this paper, the analysis and semantic annotation of images are studied. The development of methods for colour image annotations based on learning represents the main contribution of the paper. The developed algorithms generate semantic pattern rules that identify high-level image concepts. A semantic pattern rule is a combination of images’ region patterns that identifies semantic concepts. Our methods are not limited to any specific domain and they can be applied in any field.

Algorithms for reducing the semantic gap in image retrieval systems

In this paper we study the possibilities to discover correlations between visual primitive characteristics and semantic concepts of images, meaning the extraction of semantic meaning based on learning, from an image database. The problem of automatic discovery of semantic inference rules is approached. A semantic rule is a combination of semantic indicator values, which are visual elements, that identifies semantic concepts of images. The annotation procedure starts with the semantic rules generation on each image category. The language used for rules representation is Prolog. The advantages of using Prolog are its flexibility and simplicity in representation of rules. Our methods are not limited to any specific domain and they can be applied in any field.

Image Annotation Based on Semantic Rules

For developing image navigation systems, we need tools to realize the semantic relationship between user and database. In this paper we develop algorithms that automatically generate semantic rules that identify image categories and introduce the cognitive dimension in the retrieval process. The semantic rules are represented in Prolog and can be shared and modified depending on the updates in the respective domain.

Algorithms and Results in Content-Based Visual Query of the Image Databases Resulting from Dicom Files

The article presents a series of algorithms used in the content-based visual query process on databases with color medical images extracted from DICOM files provided by medical tools used in the diagnosis area. Also, the paper presents a detailed study on several directions inside the content-based visual query process. In studying the content-based image query on color feature the transformation from RGB color space to HSV and the quantization at 166 colors was used. For computing the dissimilitude between the query and the target image three metrics were implemented: the Euclidian distance, the histograms intersection and the quadratic distance between histograms. The next study was made on content-based image query on color texture feature. In order to compute the color texture characteristics vectors two methods were implemented: the co-occurrence matrices and Gabor filters. The third studied problem was the content-based region query. For detecting color regions the color set back-projection algorithm was implemented. Statistic studies for this algorithm in keeping track of the patient evolution during the treatment of the peptic ulcers were made