User participation in the categorisation of the State Library of Queensland digital image collections: characteristics, motivations and experiences

In 2010, the State Library of Queensland (SLQ) donated their out-of-copyright Queensland images to Wikimedia Commons. One direct effect of publishing the collections at Wikimedia Commons is the ability of general audiences to participate and help the library in processing the images in the collection. This paper will discuss a project that explored user participation in the categorisation of the State Library of Queensland digital image collections. The outcomes of this project can be used to gain a better understanding of user participation that lead to improving access to library digital collections. Two techniques for data collection were used: documents analysis and interview. Document analysis was performed on the Wikimedia Commons monthly reports. Meanwhile, interview was used as the main data collection technique in this research. The data collected from document analysis was used to help the researchers to devise appropriate questions for interviews. The interviews were undertaken with participants who were divided into two groups: SLQ staff members and Wikimedians (users who participate in Wikimedia). The two sets of data collected from participants were analysed independently and compared. This method was useful for the researchers to understand the differences between the experiences of categorisation from both the librarians’ and the users’ perspectives. This paper will provide a discussion on the preliminary findings that have emerged from each group participant. This research provides preliminary information about the extent of user participation in the categorisation of SLQ collections in Wikimedia Commons that can be used by SLQ and other interested libraries in describing their digital content by their categorisations to improve user access to the collection in the future.

Visual analysis of image collections

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image-Projection Explorer for Images-a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.

Interactive exploration of image collections on mobile devices

Image collections are ever growing and hence visual information is becoming more and more important. Moreover, the classical paradigm of taking pictures has changed, first with the spread of digital cameras and, more recently, with mobile devices equipped with integrated cameras. Clearly, these image repositories need to be managed, and tools for effectively and efficiently searching image databases are highly sought after, especially on mobile devices where more and more images are being stored. In this paper, we present an image browsing system for interactive exploration of image collections on mobile devices. Images are arranged so that visually similar images are grouped together while large image repositories become accessible through a hierarchical, browsable tree structure, arranged on a hexagonal lattice. The developed system provides an intuitive and fast interface for navigating through image databases using a variety of touch gestures. © 2012 Springer-Verlag.

Query Enrichment for Image Collections by Reuse of Classification Rules

User queries over image collections, based on semantic similarity, can be processed in several ways. In this paper, we propose to reuse the rules produced by rule-based classifiers in their recognition models as query pattern definitions for searching image collections.

Interactive browsing of image collections on mobile devices

Image collections are growing at a rapid rate and hence visual information is becoming more and more important. Clearly, these image repositories need to be managed, and tools for effectively and efficiently searching image databases are highly sought after, especially on mobile devices where more and more images are being stored. In this paper, we present an image browsing system for interactive exploration of image collections on mobile devices. Images are arranged so that visually similar images are grouped together while large image repositories become accessible through a hierarchical, browsable tree structure, arranged on a hexagonal lattice. The developed system provides an intuitive and fast interface for navigating through image databases using a variety of touch gestures.

Content based image retrieval using unclean positive examples

Conventional content-based image retrieval (CBIR) schemes employing relevance feedback may suffer from some problems in the practical applications. First, most ordinary users would like to complete their search in a single interaction especially on the web. Second, it is time consuming and difficult to label a lot of negative examples with sufficient variety. Third, ordinary users may introduce some noisy examples into the query. This correspondence explores solutions to a new issue that image retrieval using unclean positive examples. In the proposed scheme, multiple feature distances are combined to obtain image similarity using classification technology. To handle the noisy positive examples, a new two-step strategy is proposed by incorporating the methods of data cleaning and noise tolerant classifier. The extensive experiments carried out on two different real image collections validate the effectiveness of the proposed scheme.

Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

ANIMATED TRANSITION IN SIMILARITY-BASED TILED IMAGE LAYOUT

Effective techniques for organizing and visualizing large image collections are in growing demand as visual search gets increasingly popular. iMap is a treemap representation for visualizing and navigating image search and clustering results based on the evaluation of image similarity using both visual and textual information. iMap not only makes effective use of available display area to arrange images but also maintains stable update when images are inserted or removed during the query. A key challenge of using iMap lies in the difficult to follow and track the changes when updating the image arrangement as the query image changes. For many information visualization applications, showing the transition when interacting with the data is critically important as it can help users better perceive the changes and understand the underlying data. This work investigates the effectiveness of animated transition in a tiled image layout where the spiral arrangement of the images is based on their similarity. Three aspects of animated transition are considered, including animation steps, animation actions, and flying paths. Exploring and weighting the advantages and disadvantages of different methods for each aspect and in conjunction with the characteristics of the spiral image layout, we present an integrated solution, called AniMap, for animating the transition from an old layout to a new layout when a different image is selected as the query image. To smooth the animation and reduce the overlap among images during the transition, we explore different factors that might have an impact on the animation and propose our solution accordingly. We show the effectiveness of our animated transition solution by demonstrating experimental results and conducting a comparative user study.

Cloud Computing Service for Managing Large Medical Image Data-Sets Using Balanced Collaborative Agents

Managing large medical image collections is an increasingly demanding important issue in many hospitals and other medical settings. A huge amount of this information is daily generated, which requires robust and agile systems. In this paper we present a distributed multi-agent system capable of managing very large medical image datasets. In this approach, agents extract low-level information from images and store them in a data structure implemented in a relational database. The data structure can also store semantic information related to images and particular regions. A distinctive aspect of our work is that a single image can be divided so that the resultant sub-images can be stored and managed separately by different agents to improve performance in data accessing and processing. The system also offers the possibility of applying some region-based operations and filters on images, facilitating image classification. These operations can be performed directly on data structures in the database.

Intuitive mobile image browsing on a hexagonal lattice

Following miniaturisation of cameras and their integration into mobile devices such as smartphones combined with the intensive use of the latter, it is likely that in the near future the majority of digital images will be captured using such devices rather than using dedicated cameras. Since many users decide to keep their photos on their mobile devices, effective methods for managing these image collections are required. Common image browsers prove to be only of limited use, especially for large image sets [1].

Truly interactive approaches to browsing image databases

Image collections are ever growing and hence efficient and effective tools to manage these repositories are highly sought after. In this paper, we present effective image browsing systems that are operated on a large multi-touch environment for truly interactive exploration. Not only do image browsers pose a useful alternative to retrieval-based systems, they also provide a visualisation of the whole image collection and allow users to interactively explore particular parts of the collection. Our systems are based on the idea that visually similar images are located close to each other in the visualisation, that image thumbnails are arranged on a regular lattice (either a regular grid projected onto a sphere or a hexagonal lattice), and that large image datasets can be accessed through a hierarchical tree structure. A pilot study has shown that the presented systems do indeed work well and are preferred compared to conventional image browsers. © 2011 IEEE.

Image retrieval on the Honeycomb Image Browser

Efficient and effective approaches of dealing with the vast amount of visual information available nowadays are highly sought after. This is particularly the case for image collections, both personal and commercial. Due to the magnitude of these ever expanding image repositories, annotation of all images images is infeasible, and search in such an image collection therefore becomes inherently difficult. Although content-based image retrieval techniques have shown much potential, such approaches also suffer from various problems making it difficult to adopt them in practice. In this paper, we follow a different approach, namely that of browsing image databases for image retrieval. In our Honeycomb Image Browser, large image databases are visualised on a hexagonal lattice with image thumbnails occupying hexagons. Arranged in a space filling manner, visually similar images are located close together enabling large image datasets to be navigated in a hierarchical manner. Various browsing tools are incorporated to allow for interactive exploration of the database. Experimental results confirm that our approach affords efficient image retrieval. © 2010 IEEE.

DADAISM Project Plan

The DADAISM project brings together researchers from the diverse fields of archaeology, human computer interaction, image processing, image search and retrieval, and text mining to create a rich interactive system to address the problems of researchers finding images relevant to their research. In the age of digital photography, thousands of images are taken of archaeological artefacts. These images could help archaeologists enormously in their tasks of classification and identification if they could be related to one another effectively. They would yield many new insights on a range of archaeological problems. However, these images are currently greatly underutilized for two key reasons. Firstly, the current paradigm for interaction with image collections is basic keyword search or, at best, simple faceted search. Secondly, even if these interactions are possible, the metadata related to the majority of images of archaeological artefacts is scarce in information relating to the content of the image and the nature of the artefact, and is time intensive to enter manually. DADAISM will transform the way in which archaeologists interact with online image collections. It will deploy user-centred design methodologies to create an interactive system that goes well beyond current systems for working with images, and will support archaeologists’ tasks of finding, organising, relating and labelling images as well as other relevant sources of information such as grey literature documents.

A step-by-step guide for building localised versions of the MIC site. Version 1.1

This is a test version of the kit for localising the Moving Image Collections (MIC) site, designed in the context of a research project undertaken by the Audiovisual and Multimedia Section (AVMS) of IFLA.

Integration of Virtual Map Rooms as a support tool in historical and social research

At the present time almost all map libraries on the Internet are image collections generated by the digitization of early maps. This type of graphics files provides researchers with the possibility of accessing and visualizing historical cartographic information keeping in mind that this information has a degree of quality that depends upon elements such as the accuracy of the digitization process and proprietary constraints (e.g. visualization, resolution downloading options, copyright, use constraints). In most cases, access to these map libraries is useful only as a first approach and it is not possible to use those maps for scientific work due to the sparse tools available to measure, match, analyze and/or combine those resources with different kinds of cartography. This paper presents a method to enrich virtual map rooms and provide historians and other professional with a tool that let them to make the most of libraries in the digital era.