Abstract. This paper describes a novel interaction technique to support content-based image search in large image collections. The idea is to represent each image as a vertex in a directed graph. Given a set of image features, an arc is established between two images if there exists at least one combination of features for which one image is retrieved as the nearest neighbour of the other. Each arc is weighted by the proportion of feature combinations for which the nearest neighour relationship holds. By thus integrating the retrieval results over all possible feature combinations, the resulting network helps expose the semantic richness of images and thus provides an elegant solution to the problem of feature weighting in content-based image retrieval. We give details of the method used for network generation and describe the ways a user can interact with the structure. We also provide an analysis of the network’s topology and provide quantitative evidence for the usefulness of the technique. 1

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We present a user-centric system for visualization and layout for content-based image retrieval. Image features (visual and/or semantic) are used to display retrievals as thumbnails in a 2-D spatial layout or "configuration" which conveys all pair-wise mutual similarities. A graphical optimization technique is used to provide maximally uncluttered and informative layouts. Moreover, a novel subspace feature weighting technique can be used to modify 2-D layouts in a variety of context-dependent ways. An efficient computational technique for subspace weighting and re-estimation leads to a simple user-modeling framework whereby the system can learn to display query results based on layout examples (or relevance feedback) as provided by the user. The resulting retrieval, browsing and visualization engine can adapt to the users' (time-varying) notions of content, context and preferences in presentation style and interactive navigation. Monte Carlo simulations with machine-generated layouts as well as pilot user studies have demonstrated the ability of this framework to model or "mimic" users, by automatically generating layouts according to their preferences.

Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. In this scenario, it is necessary to develop appropriate information systems to efficiently manage these collections. The commonest approaches use the so-called Content-Based Image Retrieval (CBIR) systems. Basically, these systems try to retrieve images similar to a user-defined specification or pattern (e.g., shape sketch, image example). Their goal is to support image retrieval based on content properties (e.g., shape, color, texture), usually encoded into feature vectors. One of the main advantages of the CBIR approach is the possibility of an automatic retrieval process, instead of the traditional keyword-based approach, which usually requires very laborious and time-consuming previous annotation of database images. The CBIR technology has been used in several applications such as fingerprint identification, biodiversity information systems, digital libraries, crime prevention, medicine, historical research, among others. This paper aims to introduce the problems and challenges concerned with the creation of CBIR systems, to describe the existing solutions and applications, and to present the state of the art of the existing research in this area.

This paper deals with content-based image indexing and category retrieval in general databases. Statistical learning approaches have been recently introduced in CBIR. Labelled images are considered as training data in learning strategy based on classification process. We introduce an active learning strategy to select the most difficult images to classify with only few training data. Experimentations are carried out on the COREL database. We compare seven classification strategies to evaluate the active learning contribution in CBIR.

Abstract. This paper describes interfaces for a suite of three recently developed techniques to facilitate content-based access to large image and video repositories. Two of these techniques involve content-based retrieval while the third technique is centered around a new browsing structure and forms a useful complement to the traditional query-byexample paradigm. Each technique is associated with its own user interface and allows for a different set of user interactions. The user can move between interfaces whilst executing a particular search and thus may combine the particular strengths of the different techniques. We illustrate each of the techniques using topics from the TRECVID 2003 contest. 1

The design of a specialised query language for content based image retrieval (CBIR) provides a means of addressing many of the problems associated with commonly used query paradigms such as query-by-example and query-by-sketch. By basing such a language on an extensible ontology which encompasses both high-level and low-level image properties and relations, one can go a long way towards bridging the semantic gap between user models of saliency and relevance and those employed by a retrieval system.

Abstract. We propose an approach that unifies browsing by tags and visual features for intuitive exploration of image databases. In contrast to traditional image retrieval approaches, we utilise tags provided by users on collaborative tagging sites, complemented by simple image analysis and classification. This allows us to find new relations between data elements. We introduce the concept of a navigation map, that describes links between users, tags, and data elements for the example of the collaborative tagging site Flickr. We show that introducing similarity search based on image features yields additional links on this map. These theoretical considerations are supported by examples provided by our system, using data and tags from real Flickr users. 1

We introduce a novel relevance feedback method for content-based image retrieval and demonstrate its effectiveness using a subset of the Corel Gallery photograph collection and five low-level colour descriptors. Relevance

Retrieval of images from large image archives based solely on their visual similarity to a query image provides an exciting alternative to conventional text-based search. For content-based retrieval images are represented in terms of visual features. The question of how to combine these for similarity computation is typically addressed by eliciting relevance feedback from the user on the retrieved images. We argue in this thesis that the prevailing approach to relevance feedback suffers from three significant shortcomings: firstly, it leaves unsolved the question of how to combine features for the first retrieval; secondly, the advantage of automated content-extraction over manual annotation is greatest for large collections but if the query image is not constrained to come from the indexed collection, content-based retrieval entails imagewise comparisons leading to prohibitive response times; thirdly, users may only have vaguely defined information needs or may change their needs in the course of the interaction. The large majority of relevance feedback techniques are ill-suited for such undirected exploration. We propose a new framework of user interaction that addresses these limitations. It is centred on what we call the NN k idea. The NN k of an image are all those images that are most similar to it under some combination of features. They can be viewed as representatives of the possible