Abstract not found

Previous research in conceptual indexing methods of images has furnished us with refined theoretical frameworks characterising various aspects of images that could and should be indexed using textual descriptors. The development of digital image processing technologies has bred a brigade of content-based indexing and retrieval methods available for applications. What the users need and in what kinds of environments different indexing and retrieval methods are relevant, has remained an area of less intensive research work.

Abstract Epic is an image retrieval system that im-plements a novel spatial-querying mechanism. A user-centred, task-oriented, comparative evaluation of Epic was undertaken in which two versions of the system--one set up to enable spatial queries only, the other allowing textual queries only-were compared. Use was made of the two systems by design professionals in simulated work task situations, and quantitative and qualitative data col-lected as indicators of the levels of users ’ satisfaction. Re sults demonstrated that users often had a ‘mental image’ of a potentially satisfying picture in mind, that they were happy to express this need in visual terms, and that in doing so they preferred to have access to Epic’s spatial-querying facility. Success in obtaining statistically signif-icant results appears to support validation of the novel methodological framework adopted. 1

This paper provides an overview of current research in image information retrieval and provides an outline of areas for future research. The approach is broad and interdisciplinary and focuses on three aspects of image research (IR): text-based retrieval, content-based retrieval, and user interactions with image information retrieval systems. The review concludes with a call for image retrieval evaluation studies similar to TREC. Keywords: Information Science, Image Retrieval, CBIR, Introduction Interest in image retrieval has increased in large part due to the rapid growth of the World Wide Web. According to a recent study (Lawrence & Giles, 1999) there are 180 million images on the publicly indexable Web, a total amount of image data of about 3Tb [terabytes], and an astounding one million or more digital images are being produced every day (Jain, 93). The need to find a desired image from a collection is shared by many groups, including journalists, engineers, historians, designers...

Three types of video surrogates visual (keyframes) , verbal (keywords/phrases), and visual and verbal were designed and studied in a qualitative investigation of user cognitive processes. The results favor the combined surrogates in which verbal information and images reinforce each other, lead to better comprehension, and may actually require less processing time. The results also highlight image features users found most helpful. These findings will inform the interface design and video representation for video retrieval and browsing Keywords Multimedia information systems, abstracting methods, multimodal video surrogates, browsing, cognitive processes 1. INTRODUCTION Use of digital videos on the Web is becoming more common for education and academic communication as well as for entertainment. As video collections grow, users must be provided with effective searching and browsing tools to facilitate quick and easy access. This requires appropriate surrogates to represent original ...

This paper reports the results of a field study on journalists' practices in requesting, searching for and selecting photos in the course of their daily work. The study addresses different types of search topics common in journalistic illustration tasks, journalists' searching behaviour and the criteria they apply in selecting photos. Data were collected by observing journalists in their work and interviewing them. A sample of requests received by the archive was also analysed. The results indicate that specific needs dominate the use of newspaper photo archives. Photos of objects, themes, or abstract topics expressed in general terms were also needed, but finding them and formulating queries in these cases especially was considered problematic. The results suggest that browsing is an essential strategy in accessing digital photo archives.

Measuring image similarity is a central topic in computer vision. In this paper, we learn similarity from Flickr groups and use it to organize photos. Two images are similar if they are likely to belong to the same Flickr groups. Our approach is enabled by a fast Stochastic Intersection Kernel MAchine (SIKMA) training algorithm, which we propose. This proposed training method will be useful for many vision problems, as it can produce a classifier that is more accurate than a linear classifier, trained on tens of thousands of examples in two minutes. The experimental results show our approach performs better on image matching, retrieval, and classification than using conventional visual features. 1.

There is a substantial body of research on computer methods for managing collections of images and videos. There is little evidence that this research has had important impact on an any community yet. I use an invitation to speak on a topic on which I am not expert to air some opinions about evaluating image retrieval research. In my opinion, there is little to be gained in measuring current solutions with reference collections, because these solutions di#er so widely from user needs that the exercise becomes empty. The user studies literature is not well enough read by the image retrieval community. As a result, we tend to study somewhat artificial problems. A study of the user needs literature suggests that we will need to solve deep problems to produce useful solutions to image retrieval problems, but that there may be a need for a number of technologies that can be built in practice. I believe we should concentrate on these issues, rather than on measuring the performance of current systems.

Problems with traditional methods of image indexing [4] have led to great interest in Content-Based Image Retrieval (CBIR) techniques, which employ automatically derived features such as color, texture, and shape as search criteria [8]. The CBIR problem is a challenging topic of current research [5] and is of growing interest in medical applications such as medical records systems. The

Abstract. A novel indexing scheme for solving the problem of nearest neighbor queries in generic metric feature spaces for content-based image retrieval is proposed to break the “dimensionality curse. ” The basis for the proposed method is the partitioning of the feature dataset into clusters that are represented by single buoys. Upon submission of a query request, only a small number of clusters whose buoys are close to the query object are considered for the approximate query result, effectively cutting down the amount of data to be processed enormously. Results concerning the retrieval accuracy from extensive experimentation with a real image archive are given. The influence of control parameters is investigated with respect to the tradeoff between retrieval accuracy and computational cost. 1