Mathias Lux

Lire: lucene image retrieval: an extensible java CBIR library

LIRe (Lucene Image Retrieval) is a light weight open source Java library for content based image retrieval. It provides common and state of the art global image features and offers means for indexing and retrieval. Due to the fact that it is based on a light weight embedded text search engine, it can be integrated easily in applications without relying on a database server.

The Web 2.0 way of learning with technologies

While there is a lot of hype around various concepts associated with the term Web 2.0 in industry, little academic research has so far been conducted on the implications of this new approach for the domain of education. Much of what goes by the name of Web 2.0 can, in fact, be regarded as new kinds of learning technologies, and can be utilised as such. This paper explains the background of Web 2.0, investigates the implications for knowledge transfer in general, and then discusses its particular use in eLearning contexts with the help of short scenarios. The main challenge in the future will be to maintain essential Web 2.0 attributes, such as trust, openness, voluntariness and self-organisation, when applying Web 2.0 tools in institutional contexts.

Img(Rummager): An Interactive Content Based Image Retrieval System

This paper presents an image retrieval suite called img(Rummager) which brings into effect a number of new as well as state of the art descriptors. The application can execute an image search based on a query image, either from XML-based index ¿les, or directly from a folder containing image ¿les, extracting the comparison features in real time. In addition the img(Rummager) application can execute a hybrid search of images from the application server, combining keyword information and visual similarity. Also img(Rummager) supports easy retrieval evaluation based on the normalized modi¿ed retrieval rank (NMRR) and average precision (AP).

Caliph & Emir: MPEG-7 photo annotation and retrieval

Caliph & Emir are Java-based applications for image annotation and retrieval. They implement a large part of MPEG-7 descriptors and support annotation and retrieval based on the descriptors. Manual annotation is based on text and the MPEG-7 semantic description scheme. Automatic extraction of low level features and existing metadata is also supported. Retrieval features include: linear search, content based image retrieval, textual metadata and graph indexing, and two-dimensional repository visualization.

Content based image retrieval with LIRe

LIRe (Lucene Image Retrieval) is an open source library for content based image retrieval. Besides providing multiple common and state of the art retrieval mechanisms it allows for easy use on multiple platforms. LIRe is actively used for research, teaching and commercial applications. Due to its modular nature it can be used on process level (e.g. index images and search) as well as on image feature level. Developers and researchers can easily extend and modify LIRe to adapt it to their needs.

A novel tool for summarization of arthroscopic videos

Arthroscopic surgery is a minimally invasive procedure that uses a small camera to generate video streams, which are recorded and subsequently archived. In this paper we present a video summarization tool and demonstrate how it can be successfully used in the domain of arthroscopic videos. The proposed tool generates a keyframe-based summary, which clusters visually similar frames based on user-selected visual features and appropriate dissimilarity metrics. We discuss how this tool can be used for arthroscopic videos, taking advantage of several domain-specific aspects, without losing its ability to work on general-purpose videos. Experimental results confirm the feasibility of the proposed approach and encourage extending it to other application domains.

Visualization of video motion in context of video browsing

We present a new approach for video browsing using visualization of motion direction and motion intensity statistics by color and brightness variations. Statistics are collected from motion vectors of H.264/AVC encoded video streams, so full video decoding is not required. By interpreting visualized motion patterns of video segments, users are able to quickly identify scenes similar to a prototype scene or identify potential scenes of interest. We give some examples of motion patterns with different semantic value, including camera zooms, hill jumps of ski-jumpers, and the repeated appearance of a news speaker. In a user study we show that certain scenes of interest can be found significantly faster using our video browsing tool than using a video player with VCR-like controls.

Aspects of Broad Folksonomies

Folksonomies, collaboratively created sets of metadata, are becoming more and more important for organising information and knowledge of communites in the Web. While for a single user the difference to keyword assignment is marginal, the power of folksonomies emerges from the collaborative aspects. Folksonomies are already issue of research. Within this publication we analyse underlying statistical properties of broad folksonomies aiming to identify laws and characteristics, which allow inferring properties for folksonomy based retrieval. The actual benefit of folksonomies for retrieval and the derived methods are concluded from experiments with aggregated data from del.icio.us1.

A classification scheme for user intentions in image search

Searching for images on the web is still an open problem. While multiple approaches have been presented, there has been surprisingly little work on the actual goals and intentions of users. In this poster we present our classification scheme for user goals in image search and describe our ongoing work focusing on identification and classification of user intentions during image search tasks.

From folksonomies to ontologies: employing wisdom of the crowds to serve learning purposes

Is Web 2.0 just hype or just a buzzword, which might disappear in the near future? One way to find answers to these questions is to investigate the actual benefit of the Web 2.0 for real use cases. Within this contribution we study a very special aspect of the Web 2.0 ? the folksonomy ? and its use within self-directed learning. Guided by conceptual principles of emergent computing we point out methods, which might be able to let semantics emerge from folksonomies and discuss the effect of the results in self-directed learning.