Abstract: This paper describes the participation of the Organizer Teamin the ImageCLEFlifelog 2018 Daily Living Understanding and Lifelog MomentRetrieval. In this paper, we propose how to exploit LIFER, aninteractive lifelog search engine to solve the two tasks: Lifelog MomentRetrieval and Activities of Daily Living Understanding. We propose approachesfor both baseline, which aim to provide a reference system forother approaches, and human-in-the-loop, which advance the baselineresults.

Abstract: The number of bandwidth-hungry applications and services is constantly growing. HTTP adaptive streaming of audiovisual content accounts for the majority of today's internet traffic. Although the internet bandwidth increases also constantly, audio-visual compression technology is inevitable and we are currently facing the challenge to be confronted with multiple video codecs. This paper provides a practical evaluation of state of the art video codecs (i. e., AV1, AVC/libx264, HEVC/libx265, VP9/Iibvpx-vp9) for large-scale HTTP adaptive streaming services. In anticipation of the results, AV I shows promising performance compared to established video codecs. Additionally, AV I is intended to be royalty free making it worthwhile to be considered for large scale HTTP adaptive streaming services.

Abstract: The number of bandwidth-hungry applications and services is constantly growing. HTTP adaptive streaming of audio-visual content accounts for the majority of today's internet traffic. Although the internet bandwidth increases also constantly, audio-visual compression technology is inevitable and we are currently facing the challenge to be confronted with multiple video codecs.This paper proposes a multi-codec DASH dataset comprising AVC, HEVC, VP9, and AV1 in order to enable interoperability testing and streaming experiments for the efficient usage of these codecs under various conditions. We adopt state of the art encoding and packaging options and also provide basic quality metrics along with the DASH segments. Additionally, we briefly introduce a multi-codec DASH scheme and possible usage scenarios. Finally, we provide a preliminary evaluation of the encoding efficiency in the context of HTTP adaptive streaming services and applications.

Abstract: About 300 hours of video are uploaded to YouTube every minute. The main technology to delivery YouTube content to various clients is HTTP adaptive streaming and the majority of today's internet traffic comprises streaming audio and video. In this paper, we investigate content provisioning for HTTP adaptive streaming under predefined aspects representing content features and upload characteristics as well and apply it to YouTube. Additionally, we compare the YouTube's content upload and processing functions with a commercially available video encoding service. The results reveal insights into YouTube's content upload and processing functions and the methodology can be applied to similar services. All experiments conducted within the paper allow for reproducibility thanks to the usage of open source tools, publicly available datasets, and scripts used to conduct the experiments on virtual machines.

Abstract: Streaming audio and video content currently accounts for the majority of the internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players and adaptation algorithms showing different behavior but lack a common framework for both objective and subjective evaluation of such systems. This paper aims to close this gap by (i) proposing such a framework, (ii) describing its architecture, (iii) providing an example evaluation, (iv) and discussing open issues.

Abstract: Streaming audio and video content currently accounts for the majority of the internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players and adaptation algorithms showing different behavior but lack a common framework for both objective and subjective evaluation of such systems. This paper aims to close this gap by (i) proposing such a framework, (ii) describing its architecture, (iii) providing an example evaluation, (iv) and discussing open issues.

Abstract: The original blog post can be found at the Bitmovin Techblog and has been modified/updated here to focus on and highlight research aspects.

Abstract: Content-based analysis and retrieval of digital images found in scientific articles is often hindered by images consisting of multiple subfigures (compound figures). We address this problem by proposing a method (ComFig) to automatically classify and separate compound figures, which consists of two main steps: (i) a supervised compound figure classifier (ComFig classifier) discriminates between compound and non-compound figures using task-specific image features; and (ii) an image processing algorithm is applied to predicted compound images to perform compound figure separation (ComFig separation). The proposed ComFig classifier is shown to achieve state-of-the-art classification performance on a published dataset. Our ComFig separation algorithm shows superior separation accuracy on two different datasets compared to other known automatic approaches. Finally, we propose a method to evaluate the effectiveness of the ComFig chain combining classifier and separation algorithm, and use it to optimize the misclassification loss of the ComFig classifier for maximal effectiveness in the chain.

Abstract: We present a modern interactive video retrieval tool, called diveXplore, that has been used for several iterations of the Video Browser Showdown (VBS) competition with great success – 2nd place for the last two years in a row. The tool provides novel video content search and interaction features (e.g., a semantic map-search & browsing feature with similarity arrangement and a highly efficient sketch-search, optimized for mobile touch-interaction) that make it perfectly suited for flexible video retrieval in large video collections. With the help of a user study we show that the diveXplore system can be used very efficiently by both type of users: novices and experts. Our evaluation results do also show that the interaction statistics of novices and experts differ in terms of used features. The details of our insights can be used to further optimize interfaces of video retrieval tools for non-experts.

Abstract: In this tutorial we discuss interactive video search tools and methods, review their need in the age of deep learning, and explore video and multimedia search challenges and their role as evaluation benchmarks in the field of multimedia information retrieval. We cover three different campaigns (TRECVID, Video Browser Showdown, and the Lifelog Search Challenge), discuss their goals and rules, and present their achieved findings over the last half-decade. Moreover, we talk about datasets, tasks, evaluation procedures, and examples of interactive video search tools, as well as how they evolved over the years. Participants of this tutorial will be able to gain collective insights from all three challenges and use them for focusing their research efforts on outstanding problems that still remain unsolved in this area.