Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Психосемантичні засоби дослідження рефлексивної активності

In this article, reflectivity is considered as an individual general ability to develop different attitudes to life events in order to reduce an external and internal uncertainty in situations. The objective of the research is to examine the self-assessment criteria for reflectivity with psychosemantic procedure. The author designs a modified version of the Ch. Osgood’s (1957) Semantic Differential (SD) for examining the content and formal features of the self-assessment criteria of reflectivity. This study suggests two main processes of self-assessment of reflectivity, notably differentiation and integration. The results of factor analysis indicate that individuals with high reflectivity level are aligned with low differentiation of the semantic space and monolithic nature of self-assessment criteria. The coherence and consistency of self-assessment criteria reduce the individuals’ level of inner uncertainty, transform external problems to familiar tasks and increase an efficient decision-making. A high level of differentiation is related to individual readiness to make a correct decision in the situation of multiple choice. High differentiation increases the individual adjustment and prevents from poor effects of high reflectivity. Consequently, a high level of reflectivity is associated with a low level of differentiation of self-assessment criteria.У статті рефлексивність розглядається як загальна здатність особистості ставати у різні позиції щодо подій власної життєдіяльності задля зниження ступеня зовнішньої та внутрішньої невизначеності. Мета дослідження – випрацювання оцінних критеріїв рефлективності на основі методів психосемантики. Автор розробила процедуру часткового семантичного диференціала, придатного для оцінки змісту і формальних рис рефлективності. Застосування факторного аналізу дало змогу виокремити лише дві узагальнені вторинні характеристики формальних ознак: рівень інтегрованості та диференціації. Виявлено, що високий рівень рефлексивності пов’язаний з низькою артикульованістю семантичного простору, з вираженою монолітністю оцінних критеріїв. Узгодженість та несуперечливість оцінних критеріїв допомагає суб’єктам знижувати рівень внутрішньої невизначеності, зводити зовнішні проблеми до типових задач. Висока артикульованість семантичного простору корелює з готовністю особи до перевірки висунутих припущень у ситуації множинного вибору, з домінуванням установки на правильність прийняття рішення. У результаті, встановлено, що високий рівень рефлексивності пов’язаний із низьким рівнем диференціації критеріїв оцінювання рефлексивної активності

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

A Progressive Clustering Algorithm to Group the XML Data by Structural and Semantic Similarity

Since the emergence in the popularity of XML for data representation and exchange over the Web, the distribution of XML documents has rapidly increased. It has become a challenge for researchers to turn these documents into a more useful information utility. In this paper, we introduce a novel clustering algorithm PCXSS that keeps the heterogeneous XML documents into various groups according to their similar structural and semantic representations. We develop a global criterion function CPSim that progressively measures the similarity between a XML document and existing clusters, ignoring the need to compute the similarity between two individual documents. The experimental analysis shows the method to be fast and accurate

Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following