Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images

International audienceIn this demonstration, we present Exquisitor, a media explorer capable of learning user preferences in real-time during interactions with the 99.2 million images of YFCC100M. Exquisitor owes its efficiency to innovations in data representation, compression, and indexing. Exquisitor can complete each interaction round, including learning preferences and presenting the most relevant results, in less than 30 ms using only a single CPU core and modest RAM. In short, Exquisitor can bring large-scale interactive learning to standard desktops and laptops, and even high-end mobile devices

Towards an All-Purpose Content-Based Multimedia Information Retrieval System

The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from and browsing in mixed media collections. The effectiveness of vitrivr is shown on the basis of a user study that involves different query and media types. To the best of our knowledge, the full vitrivr stack is unique in that it is the first multimedia retrieval system that seamlessly integrates support for four different types of media. As such, it paves the way towards an all-purpose, content-based multimedia information retrieval system

Baseline analysis of a conventional and virtual reality lifelog retrieval system

Continuous media capture via a wearable devices is currently one of the most popular methods to establish a comprehensive record of the entirety of an individual's life experience, referred to in the research community as a lifelog. These vast multimodal corpora include visual and other sensor data and are enriched by content analysis, to generate as extensive a record of an individual's life experience. However, interfacing with such datasets remains an active area of research, and despite the advent of new technology and a plethora of competing mediums for processing digital information, there has been little focus on newly emerging platforms such as virtual reality. In this work, we suggest that the increase in immersion and spatial dimensions provided by virtual reality could provide significant benefits to users when compared to more conventional access methodologies. Hence, we motivate virtual reality as a viable method of exploring multimedia archives (specifically lifelogs) by performing a baseline comparative analysis using a novel application prototype built for the HTC Vive and a conventional prototype built for a standard personal computer

A novel user-centered design for personalized video summarization

In the past, several automatic video summarization systems had been proposed to generate video summary. However, a generic video summary that is generated based only on audio, visual and textual saliencies will not satisfy every user. This paper proposes a novel system for generating semantically meaningful personalized video summaries, which are tailored to the individual user's preferences over video semantics. Each video shot is represented using a semantic multinomial which is a vector of posterior semantic concept probabilities. The proposed system stitches video summary based on summary time span and top-ranked shots that are semantically relevant to the user's preferences. The proposed summarization system is evaluated using both quantitative and subjective evaluation metrics. The experimental results on the performance of the proposed video summarization system are encouraging

Preview Cues: Enhancing Access to Multimedia Content

We describe preview cues, a lightweight mechanism to assist exploration of multimedia content. A preview cue provides a preview of the kind of content/information associated with an area (as opposed to an instance) of a domain. Preview cues associate media files and their meta data with the label of a topic in a domain. A lightweight gesture such as brushing a cursor over a label initiates playback of the preview cue file associated with that label. With these cues, users can preview the type of content associated with an area of a domain in order to decide whether or not that area is of interest for further exploration before having to select it. In this paper we describe the preview cues mechanism. We look at one case study of an implementation of preview cues in the audio domain, and we present the results of a user study of preview cue deployment. We conclude with a discussion of issues for future research

Visual Information Retrieval in Endoscopic Video Archives

In endoscopic procedures, surgeons work with live video streams from the inside of their subjects. A main source for documentation of procedures are still frames from the video, identified and taken during the surgery. However, with growing demands and technical means, the streams are saved to storage servers and the surgeons need to retrieve parts of the videos on demand. In this submission we present a demo application allowing for video retrieval based on visual features and late fusion, which allows surgeons to re-find shots taken during the procedure.Comment: Paper accepted at the IEEE/ACM 13th International Workshop on Content-Based Multimedia Indexing (CBMI) in Prague (Czech Republic) between 10 and 12 June 201