Abstract: Kaoru Ota, Minh Son Dao, Vasileios Mezaris, Francesco G. B. De Natale
Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Abstract: Lorenzo Seidenari, Claudio Baecchi, Tiberio Uricchio, Andrea Ferracani, Marco Bertini, Alberto Del Bimbo
In this article, we address the problem of creating a smart audio guide that adapts to the actions and interests of museum visitors. As an autonomous agent, our guide perceives the context and is able to interact with users in an appropriate fashion. To do so, it understands what the visitor is looking at, if the visitor is moving inside the museum hall, or if he or she is talking with a friend. The guide performs automatic recognition of artworks, and it provides configurable interface features to improve the user experience and the fruition of multimedia materials through semi-automatic interaction. Our smart audio guide is backed by a computer vision system capable of working in real time on a mobile device, coupled with audio and motion sensors. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Abstract: Florian Vandecasteele, Karel Vandenbroucke, Dimitri Schuurman, Steven Verstockt
Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Multiparty videoconferences, or more generally multiparty video calls, are gaining a lot of popularity as they offer a rich communication experience. These applications have, however, large requirements in terms of both network and computational resources and have to deal with sets of heterogenous clients. The multiparty videoconferencing systems are usually either based on expensive central nodes, called Multipoint Control Units (MCU), with transcoding capabilities, or on a peer-to-peer architecture where users cooperate to distribute more efficiently the different video streams. Whereas the first class of systems requires an expensive central hardware, the second one depends completely on the redistribution capacity of the users, which sometimes might neither provide sufficient bandwidth nor be reliable enough. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Abstract: Giuseppe Cofano, Luca De Cicco, Thomas Zinner, Anh Nguyen-Ngoc, Phuoc Tran-Gia, Saverio Mascolo
This article investigates several network-assisted streaming approaches that rely on active cooperation between video streaming applications and the network. We build a Video Control Plane that enforces Video Quality Fairness among concurrent video flows generated by heterogeneous client devices. For this purpose, a max-min fairness optimization problem is solved at runtime. We compare two approaches to actuate the optimal solution in an Software Defined Networking network: The first one allocates network bandwidth slices to video flows, and the second one guides video players in the video bitrate selection. We assess performance through several QoE-related metrics, such as Video Quality Fairness, video quality, and switching frequency. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Abstract: Piotr Wisniewski, Jordi Mongay Batalla, Andrzej Beben, Piotr Krawiec, Andrzej Chydzinski
Traditionally, video adaptive algorithms aim to select the representation that better fits to the current download rate. In recent years, a number of new approaches appeared that take into account the buffer occupancy and the probability of video rebuffering as important indicators of the representation to be selected. We propose an optimization of the existing algorithm based on rebuffering probability and argue that the algorithm should avoid the situations when the client buffer is full and the download is stopped, since these situations decrease the efficiency of the algorithm. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Abstract: Jan Willem Kleinrouweler, Sergio Cabrero, Pablo Cesar
Dynamic Adaptive Streaming over HTTP (DASH) is the premier technology for Internet video streaming. DASH efficiently uses existing HTTP-based delivery infrastructures implementing adaptive streaming. However, DASH traffic is bursty in nature. This causes performance problems when DASH players share a network connection or in networks with heavy background traffic. The result is unstable and lower quality video. In this article, we present the design and implementation of a so-called DASH Assisting Network Element (DANE). Our system provides target bitrate signaling and dynamic traffic control. These two mechanisms realize proper bandwidth sharing among clients. Our system is privacy friendly and fully supports encrypted video streams. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Abstract: Priyanka Singh, Balasubramanian Raman, Nishant Agarwal, Pradeep K. Atrey
The benefits of high-end computation infrastructure facilities provided by cloud-based multimedia systems are attracting people all around the globe. However, such cloud-based systems possess security issues as third party servers become involved in them. Rendering data in an unreadable form so that no information is revealed to the cloud data centers will serve as the best solution to these security issues. One such image encryption scheme based on a Permutation Ordered Binary Number System has been proposed in this work. It distributes the image information in totally random shares, which can be stored at the cloud data centers. Further, the proposed scheme authenticates the shares at the pixel level. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms have been developed in the fully supervised setting, requiring access to a large amount of labeled training data. However, the main bottleneck for fully supervised re-ID is the limited availability of labeled training samples. To address this problem, we propose a self-trained subspace learning paradigm for person re-ID that effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
This article addresses the problem of recognizing partially observed human actions. Videos of actions acquired in the real world often contain corrupt frames caused by various factors. These frames may appear irregularly, and make the actions only partially observed. They change the appearance of actions and degrade the performance of pretrained recognition systems. In this article, we propose an approach to address the corrupt-frame problem without knowing their locations and durations in advance. The proposed approach includes two key components: outlier filtering and observation completion. The former identifies and filters out unobserved frames, and the latter fills up the filtered parts by retrieving coherent alternatives from training data. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
The film industry continuously strives to make visitors’ movie experience more immersive and thus, more captivating. This is realized through larger screens, sophisticated speaker systems, and high quality 2D and 3D content. Moreover, a recent trend in the film industry is to incorporate multiple interaction modalities, such as 4D film, to simulate rain, wind, vibration, and heat, in order to intensify viewers’ emotional reactions. In this context, humans’ sense of touch possesses significant potential for intensifying emotional reactions for the film experience beyond audio-visual sensory modalities. This article presents a framework for authoring tactile cues (tactile gestures as used in this article) and enabling automatic rendering of said gestures to intensify emotional reactions in an immersive film experience. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Many social media applications, such as recommendation, virality prediction, and marketing, make use of user gender, which may not be explicitly specified or kept privately. Meanwhile, advanced mobile devices have become part of our lives and a huge amount of content is being generated by users every day, especially user shared images shared by individuals in social networks. This particular form of user generated content is widely accessible to others due to the sharing nature. When user gender is only accessible to exclusive parties, these user shared images are proved to be an easier way to identify user gender. This work investigated 3,152,344 images by 7,450 users from Fotolog and Flickr, two image-oriented social networks. PubDate: Wed, 28 Jun 2017 00:00:00 GMT
Abstract: Ishwarya Thirunarayanan, Khimya Khetarpal, Sanjeev Koppal, Olivier Le Meur, John Shea, Eakta Jain
Traditional comics are increasingly being augmented with digital effects, such as recoloring, stereoscopy, and animation. An open question in this endeavor is identifying where in a comic panel the effects should be placed. We propose a fast, semi-automatic technique to identify effects-worthy segments in a comic panel by utilizing gaze locations as a proxy for the importance of a region. We take advantage of the fact that comic artists influence viewer gaze towards narrative important regions. By capturing gaze locations from multiple viewers, we can identify important regions and direct a computer vision segmentation algorithm to extract these segments. The challenge is that these gaze data are noisy and difficult to process. PubDate: Wed, 31 May 2017 00:00:00 GMT
Abstract: Michael E. Houle, Xiguo Ma, Vincent Oria, Jichao Sun
This article presents an efficient and totally unsupervised content-based similarity search method for multimedia data objects represented by high-dimensional feature vectors. The assumption is that the similarity measure is applicable to feature vectors of arbitrary length. During the offline process, different sets of features are selected by a generalized version of the Laplacian Score in an unsupervised way for individual data objects in the database. Online retrieval is performed by ranking the query object in the feature spaces of candidate objects. Those candidates for which the query object is ranked highly are selected as the query results. The ranking scheme is incorporated into an automated query expansion framework to further improve the semantic quality of the search result. PubDate: Wed, 31 May 2017 00:00:00 GMT
Abstract: Michael Riegler, Konstantin Pogorelov, Sigrun Losada Eskeland, Peter Thelin Schmidt, Zeno Albisser, Dag Johansen, Carsten Griwodz, Pål Halvorsen, Thomas De Lange
Holistic medical multimedia systems covering end-to-end functionality from data collection to aided diagnosis are highly needed, but rare. In many hospitals, the potential value of multimedia data collected through routine examinations is not recognized. Moreover, the availability of the data is limited, as the health care personnel may not have direct access to stored data. However, medical specialists interact with multimedia content daily through their everyday work and have an increasing interest in finding ways to use it to facilitate their work processes. In this article, we present a novel, holistic multimedia system aiming to tackle automatic analysis of video from gastrointestinal (GI) endoscopy. PubDate: Wed, 31 May 2017 00:00:00 GMT
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