Abstract: The actual content of endoscopic videos is typically limited to a circular area in the image center. This area has a dynamic position and size and is surrounded by a dark, but noisy border. In this paper we present a novel algorithm that (1) classifies which frames of an endoscopic video feature the circular content area and (2) determines its exact position and size, if present. This information is very useful for improving the performance of subsequent analysis techniques. It can also be used for more efficient video encoding and economic printing of still images in findings and reports. The evaluation shows that the proposed method is very accurate, robust and efficient in terms of runtime.

Abstract: We present an improved version of last year’s winner of the Video Browser Showdown. In a preprocessing step video segments are detected and clustered in several latent classes of similar content based on color and motion information. The navigation bars of our video browser are then augmented with different colors indicating where elements of the detected clusters are located. As humans are able to classify the content of clusters fast, they can benefit from this information when browsing through a video.

Abstract: While accuracy and speed get a lot of attention in video retrieval research, the investigation of interactive retrieval tools gets less attention and is often regarded as trivial. We want to show that even simple ideas have potential to improve the retrieval performance by giving some automated support to the browsing user. We present a video browsing concept where video segments are clustered in several latent classes of similar content. The navigation bars of our video browser are augmented with different colors indicating where elements of these clusters are located. As humans are able to classify the content of clusters fast, they can benefit from this information when browsing a video. We present a study where we investigated how humans can be supported in different video browsing tasks with a color-based and a motion-based clustering of video content.

Abstract: The Video Browser Showdown (VBS) is a live competition for evaluating video browsing tools regarding their efficiency at known-item search (KIS) tasks. The first VBS was held at MMM 2012 with eight teams working on 14 tasks, of which eight were completed by expert users and six by novices. We describe the details of the competition, analyze results regarding the performance of tools, the differences between the tasks and the nature of the false submissions.

Abstract: Why do people record videos and share them? While the question seems to be simple, user intentions have not yet been investigated for video production and sharing. A general taxonomy would lead to adapted information systems and multimedia interfaces tailored to the users' intentions. We contribute (1) an exploratory user study with 20 participants, examining the various facets of user intentions for video production and sharing in detail and (2) a novel set of user intention clusters for video production, grounded empirically in our study results. We further reflect existing work in specialized domains (i.e. video blogging and mobile phone cameras) and show that prevailing models used in other multimedia fields (e.g. photography) cannot be used as-is to reason about video recording and sharing intentions.

Abstract: We propose a video browser facilitating known-item search in a single video. Key frames are presented as four images at a time and can be navigated quickly in both forward and backward directions using a slider. Alternatively, key frames can be displayed automatically at different frame rates. The user may choose between three mappings of key frames to the four key frame widgets based on video time stamps and color similarity.

Abstract: In this paper we evaluate a 3D cylindrical interface that arranges image thumbnails by visual similarity for the purpose of image browsing. Through a user study we compare the performance of this interface to the performance of a common scrollable 2D list of thumbnails in a grid arrangement. Our evaluation shows that the 3D Cylinder interface enables significantly faster visual search and is the preferred search interface for the majority of tested users.

Abstract: With users increasingly using their mobile devices such as smartphones as digital photo albums, effective methods for managing these collections are becoming increasingly important. Standard solutions provide only limited facilities for organising, browsing and searching image collections on mobile devices, making it challenging and time-consuming to locate images of interest. In this demo paper, we present an intuitive interface for organising and browsing image collections on mobile devices. Images are arranged on a 3D globe according to colour similarity. To avoid image overlap image thumbnails are placed on a regular grid structure while large image collections are organised using a hierarchical data structure. Through multi-touch user interaction image browsing can be performed in an intuitive and effective manner.

Abstract: Interactive image and video search tools typically use a grid-like arrangement of thumbnails for preview purpose. Such a display, which is commonly known as storyboard, provides limited flexibility at interactive search and it does not optimally exploit the available screen estate. In this paper we design and evaluate alternatives to the common two-dimensional storyboard. We take advantage of 3D graphics in order to present image thumbnails in cylindrical arrangements. Through a user study we evaluate the performance of these interfaces in terms of visual search time and subjective performance.

Abstract: The actual content of endoscopic videos is typically limited to a circular area in the center of the image due to the inherent characteristics of the camera. This area is surrounded by a dark border that fills up the remainder of the rectangular image and is subject to noise. The position and size of the circle is not standardized and usually varies over time. In this paper a robust algorithm is presented that (1) classifies which parts of an endoscopic video feature a circular content area and (2) determines its exact position and size, if present. This information is useful for improving video encoding efficiency, limiting further analysis steps to the relevant area and saving ink when printing still images on findings. Our evaluation shows that the proposed method is very fast, reliable and robust. Moreover, it indicates that by exploiting this information for video encoding a considerable bitrate reduction is possible with the same visual quality.

Abstract: This paper demonstrates a novel 3D Video Browser (3VB) that enables interactive search within a single video as well as video collections by utilizing 3D projection and an intuitive interaction. The browsing approach is based on hierarchical search, which means that the user can split a video into several segments. The 3VB disposes a convenient interface that allows flexible arrangement of video segments in the 3D space. It allows for concurrent playback of video segments and flexible inspection of these segments at any desired level of detail through convenient user interaction.

Abstract: Manual image annotation is a tedious and time-consuming task, while automated methods are error prone and limited in their results. Human computation, and especially games with a purpose, have shown potential to create high quality annotations by "hiding the complexity" of the actual annotation task and employing the "wisdom of the crowds". In this demo paper we present two games with a single purpose: finding regions in images that correspond to given terms. We discuss approach, implementation, and preliminary results of our work and give an outlook to immediate future work.

Abstract: Content-based retrieval systems leverage low level features such as color, texture or local information of images to find similar images to a respective query image. In recent years the Bag of Visual Words (BoVW) approach, which relies on quantized visual information around local image patches, has gained importance in image retrieval. In this paper we focus on fuzzy algorithms, in order to improve the descriptiveness of image descriptors. We extend the BoVW approach by applying fuzzy clustering and fuzzy assignment to take a step towards more effective visual descriptors, which are matched against each other in content-based similarity searches.

Abstract: In this report we describe our approach to the known-item search task for TRECVID 2012. We describe how we index available metadata and how we gain additional information about the videos using content-based analysis. A rule-based query expansion and query reduction method is applied to increase the number of relevant videos in automatic runs. Furthermore, we describe an approach for quick, interactive filtering of large result sets. We outline how the parameters of our system were tuned for the IACC dataset and discuss our TRECVID 2012 KIS results.

Abstract: In this paper, we investigate whether community-contributed multimedia content can be used to make video summaries of social events. We implemented an event summarization algorithm that uses photos from Flickr and videos from YouTube to compose summaries of well-known society events, which took place in the last three years. The comparison with a manually obtained ground truth shows a good coverage of the most important situations of these events. We do not claim to produce the best summaries possible, which may be compared to the work of a human director, but we analyze what can be achieved with community-contributed content by now.

Abstract: We participate in the Video Browser Showdown with our easy-to-use video browsing tool. It can be used for getting a quick overview of videos as well as for simple Known Item Search (KIS) tasks. It offers a parallel and a tree-like browsing interface for navigating through the content of single videos or even small video collections in a hierarchical, non-sequential manner. We want to validate whether simple KIS tasks can be completed without a time consuming content analysis in advance.

Abstract: We present an algorithm for the summarization of social events with community-contributed content from Flickr and YouTube. A clustering algorithm groups content related to the searched event. Date information, GPS coordinates, user ratings and visual features are used to select relevant photos and videos. The composed event summaries are presented with our video browser.