An information-driven framework for image mining

[Abstract]: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how low-level, pixel representation contained in a raw image or image sequence can be processed to identify high-level spatial objects and relationships. To meet this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful patterns/knowledge from each level

Choosing and using methodological search filters : searchers' views

© 2014 The authors. Health Information and Libraries Journal © 2014 Health Libraries Group.Peer reviewedPostprin

Combining information seeking services into a meta supply chain of facts

The World Wide Web has become a vital supplier of information that allows organizations to carry on such tasks as business intelligence, security monitoring, and risk assessments. Having a quick and reliable supply of correct facts from perspective is often mission critical. By following design science guidelines, we have explored ways to recombine facts from multiple sources, each with possibly different levels of responsiveness and accuracy, into one robust supply chain. Inspired by prior research on keyword-based meta-search engines (e.g., metacrawler.com), we have adapted the existing question answering algorithms for the task of analysis and triangulation of facts. We present a first prototype for a meta approach to fact seeking. Our meta engine sends a user's question to several fact seeking services that are publicly available on the Web (e.g., ask.com, brainboost.com, answerbus.com, NSIR, etc.) and analyzes the returned results jointly to identify and present to the user those that are most likely to be factually correct. The results of our evaluation on the standard test sets widely used in prior research support the evidence for the following: 1) the value-added of the meta approach: its performance surpasses the performance of each supplier, 2) the importance of using fact seeking services as suppliers to the meta engine rather than keyword driven search portals, and 3) the resilience of the meta approach: eliminating a single service does not noticeably impact the overall performance. We show that these properties make the meta-approach a more reliable supplier of facts than any of the currently available stand-alone services

ImageSieve: Exploratory search of museum archives with named entity-based faceted browsing

Over the last few years, faceted search emerged as an attractive alternative to the traditional "text box" search and has become one of the standard ways of interaction on many e-commerce sites. However, these applications of faceted search are limited to domains where the objects of interests have already been classified along several independent dimensions, such as price, year, or brand. While automatic approaches to generate faceted search interfaces were proposed, it is not yet clear to what extent the automatically-produced interfaces will be useful to real users, and whether their quality can match or surpass their manually-produced predecessors. The goal of this paper is to introduce an exploratory search interface called ImageSieve, which shares many features with traditional faceted browsing, but can function without the use of traditional faceted metadata. ImageSieve uses automatically extracted and classified named entities, which play important roles in many domains (such as news collections, image archives, etc.). We describe one specific application of ImageSieve for image search. Here, named entities extracted from the descriptions of the retrieved images are used to organize a faceted browsing interface, which then helps users to make sense of and further explore the retrieved images. The results of a user study of ImageSieve demonstrate that a faceted search system based on named entities can help users explore large collections and find relevant information more effectively