An interface to retrieve personal memories using an iconic visual language

Relevant past events can be remembered when visualizing related pictures. The main difficulty is how to find these photos in a large personal collection. Query definition and image annotation are key issues to overcome this problem. The former is relevant due to the diversity of the clues provided by our memory when recovering a past moment and the later because images need to be annotated with information regarding those clues to be retrieved. Consequently, tools to recover past memories should deal carefully with these two tasks. This paper describes a user interface designed to explore pictures from personal memories. Users can query the media collection in several ways and for this reason an iconic visual language to define queries is proposed. Automatic and semi-automatic annotation is also performed using the image content and the audio information obtained when users show their images to others. The paper also presents the user interface evaluation based on tests with 58 participants

An explorative study of interface support for image searching

In this paper we study interfaces for image retrieval systems. Current image retrieval interfaces are limited to providing query facilities and result presentation. The user can inspect the results and possibly provide feedback on their relevance for the current query. Our approach, in contrast, encourages the user to group and organise their search results and thus provide more fine-grained feedback for the system. It combines the search and management process, which - according to our hypothesis - helps the user to onceptualise their search tasks and to overcome the query formulation problem. An evaluation, involving young design-professionals and di®erent types of information seeking scenarios, shows that the proposed approach succeeds in encouraging the user to conceptualise their tasks and that it leads to increased user satisfaction. However, it could not be shown to increase performance. We identify the problems in the current setup, which when eliminated should lead to more effective searching overall

Low prevalence of valvular heart disease in 226 phentermine-fenfluramine protocol subjects prospectively followed for up to 30 months

AbstractOBJECTIVESThis investigation sought to determine the effect of phentermine-fenfluramine (phen-fen) on the prevalence of valvular heart disease in 226 obese subjects enrolled in a prospective, strict weight loss, research protocol.BACKGROUNDEarly reports have suggested that the use of phen-fen for weight loss may be associated with increased valvular heart disease. Such reports were based on small numbers of patients, limited data on dose and duration of phen-fen therapy, and no correlation with matched controls.METHODSAll subjects underwent transthoracic echocardiography for significant valvular lesions within a mean of 97 days from the manufacturer’s announcement of the voluntary withdrawal of fenfluramine and dexfenfluramine. All echocardiograms were interpreted by two independent readers.RESULTSThe study population included 183 women and 43 men with a mean age of 46.9 ± 8.9 years and mean starting body mass index of 39.8 ± 7.7 kg/m2. Using the Food and Drug Administration criteria, significant aortic regurgitation was detected in 15 subjects (6.6%) and mitral regurgitation in 3 subjects (1.3%). Only one patient had significant regurgitation of both aortic and mitral valves. No valves had severe regurgitation. Significant valvular disease did not correlate with the dose or duration of phen-fen therapy. Furthermore, the prevalence of valvular regurgitation is comparable to the normal offspring in the Framingham Heart Study, who are similar in age, gender, and geographical location.CONCLUSIONSPhen-fen therapy is associated with a low prevalence of significant valvular regurgitation. Valvular regurgitation in our subjects may reflect age-related degenerative changes

Prosemantic features for content-based image retrieval

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-18449-9_8Revised Selected Papers of 7th International Workshop, AMR 2009, Madrid, Spain, September 24-25, 2009We present here, an image description approach based on prosemantic features. The images are represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Prosemantic features are obtained by packing together the scores. To verify the effectiveness of the approach, we designed a target search experiment in which both low-level and prosemantic features are embedded into a content-based image retrieval system exploiting relevance feedback. The experiments show that the use of prosemantic features allows for a more successful and quick retrieval of the query images