Short user-generated videos classification using accompanied audio categories

This paper investigates the classification of short user-generated videos (UGVs) using the accompanied audio data since short UGVs accounts for a great proportion of the Internet UGVs and many short UGVs are accompanied by singlecategory soundtracks. We define seven types of UGVs corresponding to seven audio categories respectively. We also investigate three modeling approaches for audio feature representation, namely, single Gaussian (1G), Gaussian mixture (GMM) and Bag-of-Audio-Word (BoAW) models. Then using Support Vector Machine (SVM) with three different distance measurements corresponding to three feature representations, classifiers are trained to categorize the UGVs. The accompanying evaluation results show that these approaches are effective for categorizing the short UGVs based on their audio track. Experimental results show that a GMM representation with approximated Bhattacharyya distance (ABD) measurement produces the best performance, and BoAW representation with chi-square kernel also reports comparable results

Towards developing a collaborative video platform for learning

The work presented in this paper outlines issues relating to the development of a collaborative video platform for learning. Student adoption of collaborative and video technology is increasing dramatically, becoming part of their everyday lives. The aim of this paper is to propose a system and framework for the successful integration of these technologies into teaching and learning. At the outset we assess current trends and previous research, using these findings to inform the development of a new platform. System specifications are then presented with specific needs identified for students and educators. Finally our tentative framework for a integrating a collaborative video platform for learning is presented

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

Improving the evaluation of web search systems

Linkage analysis as an aid to web search has been assumed to be of significant benefit and we know that it is being implemented by many major Search Engines. Why then have few TREC participants been able to scientifically prove the benefits of linkage analysis over the past three years? In this paper we put forward reasons why disappointing results have been found and we identify the linkage density requirements of a dataset to faithfully support experiments into linkage analysis. We also report a series of linkage-based retrieval experiments on a more densely linked dataset culled from the TREC web documents

Evaluation of linkage-based web discovery systems

In recent years, the widespread use of the WWW has brought information retrieval systems into the homes o f many millions people. Today, we have access to many billions o f documents (web pages) and have (free-of-charge) access to powerful, fast and highly efficient search facilities over these documents provided by search engines such as Google. The "first generation" of web search engines addressed the engineering problems o f web spidering and efficient searching for large numbers o f both users and documents, but they did not innovate much in the approaches taken to searching. Recently, however, linkage analysis has been incorporated into search engine ranking strategies. Anecdotally, linkage analysis appears to have improved retrieval effectiveness o f web search, yet there is little scientific evidence in support o f the claims for better quality retrieval, which is surprising. Participants in the three most recent TREC conferences (1999, 2000 and 2001) have been invited to perform benchmarking o f information retrieval systems on web data and have had the option o f using linkage information as part of their retrieval strategies. The general consensus from the experiments of these participants is that linkage information has not yet been successfully incorporated into conventional retrieval strategies. In this thesis, we present our research into the field o f linkage-based retrieval of web documents. We illustrate that (moderate) improvements in retrieval performance is possible if the undedying test collection contains a higher link density than the test collections used in the three most recent TREC conferences. We examine the linkage structure o f live data from the WWW and coupled with our findings from crawling sections o f the WWW we present a list o f five requirements for a test collection which is to faithfully support experiments into linkage-based retrieval o f documents from the WWW. We also present some o f our own, new, vanants on linkage-based web retrieval and evaluate their performance in comparison to the approaches o f others

Information retrieval challenges of maintaining a context-aware human digital memory

The volume of personal digital data captured from today's content creation devices, such as digital cameras, digital video recorders and sensecams pose many challenges for organising and retrieving content for users. By utilising content and contextual analysis along with an understanding of the usage scenarios involved, it is possible to develop effective information retrieval technologies for these personal archives. In this talk I will discuss how we, at the Centre for Digital Video Processing, Dublin City University, have employed both content and contextual analysis to automatically organise human digital memory (sensecam) collections and I will focus specifically on how we have employed techniques from photo and video retrieval in the novel domain of human digital memories

Living with SenseCam : Experiences, motivations and advances

Being a long-term Sensecam wearer provides one with a unique insight into the benefits of, and challenges of wearing an always-on, passive, life capture device. These experiences motivate this talk, as well as a passion for researching the technical challenges of Personal Life Archives. In this keynote I will discuss my own motivation for gathering a Personal Life Archive as well as what I have learned from this process. I will motivate and describe the technical challenges to be addressed and introduce the research to address these challenges, research that points to the potential advances when cognitive science meets computer science. Finally, I will introduce the Senseseer platform, in development within DCU, which aims to efficiently gather a flexible and extensible Personal Life Archive

Beyond single-shot text queries: bridging the gap(s) between research communities

This workshop brings together researchers from different streams and communities that deal with information access in the widest sense. The general goal is to foster collaboration between the different communities and to showcase research that sits at the border between different areas of research

iForgot: a model of forgetting in robotic memories

Much effort has focused in recent years on developing more life-like robots. In this paper we propose a model of memory for robots, based on human digital memories, though our model incorporates an element of forgetting to ensure that the robotic memory appears more human and therefore can address some of the challenges for human-robot interaction

Interactive searching and browsing of video archives: using text and using image matching

Over the last number of decades much research work has been done in the general area of video and audio analysis. Initially the applications driving this included capturing video in digital form and then being able to store, transmit and render it, which involved a large effort to develop compression and encoding standards. The technology needed to do all this is now easily available and cheap, with applications of digital video processing now commonplace, ranging from CCTV (Closed Circuit TV) for security, to home capture of broadcast TV on home DVRs for personal viewing. One consequence of the development in technology for creating, storing and distributing digital video is that there has been a huge increase in the volume of digital video, and this in turn has created a need for techniques to allow effective management of this video, and by that we mean content management. In the BBC, for example, the archives department receives approximately 500,000 queries per year and has over 350,000 hours of content in its library. Having huge archives of video information is hardly any benefit if we have no effective means of being able to locate video clips which are of relevance to whatever our information needs may be. In this chapter we report our work on developing two specific retrieval and browsing tools for digital video information. Both of these are based on an analysis of the captured video for the purpose of automatically structuring into shots or higher level semantic units like TV news stories. Some also include analysis of the video for the automatic detection of features such as the presence or absence of faces. Both include some elements of searching, where a user specifies a query or information need, and browsing, where a user is allowed to browse through sets of retrieved video shots. We support the presentation of these tools with illustrations of actual video retrieval systems developed and working on hundreds of hours of video content