Interactive retrieval of video using pre-computed shot-shot similarities

A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision

EGO: a personalised multimedia management tool

The problems of Content-Based Image Retrieval (CBIR) sys- tems can be attributed to the semantic gap between the low-level data representation and the high-level concepts the user associates with images, on the one hand, and the time-varying and often vague nature of the underlying information need, on the other. These problems can be addressed by improving the interaction between the user and the system. In this paper, we sketch the development of CBIR interfaces, and introduce our view on how to solve some of the problems of the studied interfaces. To address the semantic gap and long-term multifaceted information needs, we propose a "retrieval in context" system. EGO is a tool for the management of image collections, supporting the user through personalisation and adaptation. We will describe how it learns from the user's personal organisation, allowing it to recommend relevant images to the user. The recommendation algorithm is detailed, which is based on relevance feedback techniques

That obscure object of desire: multimedia metadata on the Web, part 1

This article discusses the state of the art in metadata for audio-visual media in large semantic networks, such as the Semantic Web. Our discussion is predominantly motivated by the two most widely known approaches towards machine-processable and semantic-based content description, namely the Semantic Web activity of the W3C and ISO's efforts in the direction of complex media content modeling, in particular the Multimedia Content Description Interface (MPEG-7). We explain that the conceptual ideas and technologies discussed in both approaches are essential for the next step in multim

Multimedia resource discovery

This chapter examines the challenges and opportunities of Multimedia Information Retrieval and corresponding search engine applications. Computer technology has changed our access to information tremendously: We used to search authors or titles (which we had to know) in library cards in order to locate relevant books; now we can issue keyword searches within the full text of whole book repositories in order to identify authors, titles and locations of relevant books. What about the corresponding challenge of finding multimedia by fragments, examples and excerpts? Rather than asking for a music piece by artist and title, can we hum its tune to find it? Can doctors submit scans of a patient to identify medically similar images of diagnosed cases in a database? Can your mobile phone take a picture of a statue and tell you about its artist and significance via a service that it sends this picture to? In an attempt to answer some of these questions we get to know basic concepts of multimedia resource discovery technologies for a number of different query and document types: piggy-back text search, i.e., reducing the multimedia to pseudo text documents; automated annotation of visual components; content-based retrieval where the query is an image; and fingerprinting to match near duplicates. Some of the research challenges are given by the semantic gap between the simple pixel properties computers can readily index and high-level human concepts; related to this is an inherent technological limitation of automated annotation of images from pixels alone. Other challenges are given by polysemy, i.e., the many meanings and interpretations that are inherent in visual material and the corresponding wide range of a user’s information need. This chapter demonstrates how these challenges can be tackled by automated processing and machine learning and by utilising the skills of the user, for example through browsing or through a process that is called relevance feedback, thus putting the user at centre stage. The latter is made easier by “added value” technologies, exemplified here by summaries of complex multimedia objects such as TV news, information visualisation techniques for document clusters, visual search by example, and methods to create browsable structures within the collection

Visualization and User-Modeling for Browsing Personal Photo Libraries

Abstract. 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) provided by the user. The resulting retrieval, browsing and visualization can adapt to the user's (time-varying) notions of content, context and preferences in 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