Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

"You Tube and I Find" - personalizing multimedia content access

Recent growth in broadband access and proliferation of small personal devices that capture images and videos has led to explosive growth of multimedia content available everywhereVfrom personal disks to the Web. While digital media capture and upload has become nearly universal with newer device technology, there is still a need for better tools and technologies to search large collections of multimedia data and to find and deliver the right content to a user according to her current needs and preferences. A renewed focus on the subjective dimension in the multimedia lifecycle, fromcreation, distribution, to delivery and consumption, is required to address this need beyond what is feasible today. Integration of the subjective aspects of the media itselfVits affective, perceptual, and physiological potential (both intended and achieved), together with those of the users themselves will allow for personalizing the content access, beyond today’s facility. This integration, transforming the traditional multimedia information retrieval (MIR) indexes to more effectively answer specific user needs, will allow a richer degree of personalization predicated on user intention and mode of interaction, relationship to the producer, content of the media, and their history and lifestyle. In this paper, we identify the challenges in achieving this integration, current approaches to interpreting content creation processes, to user modelling and profiling, and to personalized content selection, and we detail future directions. The structure of the paper is as follows: In Section I, we introduce the problem and present some definitions. In Section II, we present a review of the aspects of personalized content and current approaches for the same. Section III discusses the problem of obtaining metadata that is required for personalized media creation and present eMediate as a case study of an integrated media capture environment. Section IV presents the MAGIC system as a case study of capturing effective descriptive data and putting users first in distributed learning delivery. The aspects of modelling the user are presented as a case study in using user’s personality as a way to personalize summaries in Section V. Finally, Section VI concludes the paper with a discussion on the emerging challenges and the open problems

Semiotic Annotation of Narrative Video Commercials: Bridging the Gap between Artifacts and Ontologies

Drawing on semiotic theories, the paper proposes a new concept of annotation \u2013 called semiotic annotation \u2013 whose goal is to describe the multilayered articulation of meaning inscribed within narrative video commercials by their designers. The approach exploits the use of a meta-model of the narrative video genre providing the conceptualizations and the vocabulary for analysis and annotation. By explicating design knowledge embodied in the video, semiotic annotation plays the role of intermediate level knowledge between the meta-model (an informal ontology) and practice (the concrete video artifact). In order to assess the feasibility of the approach, a test bed is presented and results are reported. A final discussion about the potential contribution of semiotic annotation in the fields of Research Through Design, Technological Mediation, and Interface Criticism concludes the study

Multiple Media Correlation: Theory and Applications

This thesis introduces multiple media correlation, a new technology for the automatic alignment of multiple media objects such as text, audio, and video. This research began with the question: what can be learned when multiple multimedia components are analyzed simultaneously? Most ongoing research in computational multimedia has focused on queries, indexing, and retrieval within a single media type. Video is compressed and searched independently of audio, text is indexed without regard to temporal relationships it may have to other media data. Multiple media correlation provides a framework for locating and exploiting correlations between multiple, potentially heterogeneous, media streams. The goal is computed synchronization, the determination of temporal and spatial alignments that optimize a correlation function and indicate commonality and synchronization between media objects. The model also provides a basis for comparison of media in unrelated domains. There are many real-world applications for this technology, including speaker localization, musical score alignment, and degraded media realignment. Two applications, text-to-speech alignment and parallel text alignment, are described in detail with experimental validation. Text-to-speech alignment computes the alignment between a textual transcript and speech-based audio. The presented solutions are effective for a wide variety of content and are useful not only for retrieval of content, but in support of automatic captioning of movies and video. Parallel text alignment provides a tool for the comparison of alternative translations of the same document that is particularly useful to the classics scholar interested in comparing translation techniques or styles. The results presented in this thesis include (a) new media models more useful in analysis applications, (b) a theoretical model for multiple media correlation, (c) two practical application solutions that have wide-spread applicability, and (d) Xtrieve, a multimedia database retrieval system that demonstrates this new technology and demonstrates application of multiple media correlation to information retrieval. This thesis demonstrates that computed alignment of media objects is practical and can provide immediate solutions to many information retrieval and content presentation problems. It also introduces a new area for research in media data analysis

Improving instance search performance in video collections

This thesis presents methods to improve instance search and enhance user performance while browsing unstructured video collections. Through the use of computer vision and information retrieval techniques, we propose novel solutions to analyse visual content and build a search algorithm to address the challenges of visual instance search, while considering the constraints for practical applications. Firstly, we investigate methods to improve the effectiveness of instance search systems for finding object instances which occurred in unstructured video content. Using the bag of feature framework, we propose a novel algorithm to use the geometric correlation information between local features to improve the accuracy of local feature matching, thus improve the performance of instance search systems without introducing much computation cost. Secondly, we consider the scenario that the performance of instance search systems may drop due to the volume of visual content in large video collections. We introduce a search algorithm based on embedded coding to increase the effectiveness and efficiency of instance search systems. And we participate in the international video evaluation campaign, TREC Video Retrieval Evaluation, to comparatively evaluate the performance of our proposed methods. Finally, the exploration and navigation of visual content when browsing large unstructured video collections is considered. We propose methods to address such challenges and build an interactive video browsing tool to improve user performance while seeking interesting content over video collections. We construct a structured content representation with similarity graph using our proposed instance search technologies. Considering the constraints related to real world usability, we present a flexible interface based on faceted navigation to enhance user performance when completing video browsing tasks. This thesis shows that user performance can be enhanced by improving the effectiveness of instance search approaches, when seeking information in unstructured video collection. While covering many different aspects of improving instance search in this work, we outline three potential directions for future work: advanced feature representation, data driven rank and cloud-based search algorithms

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

A Literature Study On Video Retrieval Approaches

A detailed survey has been carried out to identify the various research articles available in the literature in all the categories of video retrieval and to do the analysis of the major contributions and their advantages, following are the literature used for the assessment of the state-of-art work on video retrieval. Here, a large number of papershave been studied

Personalised video retrieval: application of implicit feedback and semantic user profiles

A challenging problem in the user profiling domain is to create profiles of users of retrieval systems. This problem even exacerbates in the multimedia domain. Due to the Semantic Gap, the difference between low-level data representation of videos and the higher concepts users associate with videos, it is not trivial to understand the content of multimedia documents and to find other documents that the users might be interested in. A promising approach to ease this problem is to set multimedia documents into their semantic contexts. The semantic context can lead to a better understanding of the personal interests. Knowing the context of a video is useful for recommending users videos that match their information need. By exploiting these contexts, videos can also be linked to other, contextually related videos. From a user profiling point of view, these links can be of high value to recommend semantically related videos, hence creating a semantic-based user profile. This thesis introduces a semantic user profiling approach for news video retrieval, which exploits a generic ontology to put news stories into its context. Major challenges which inhibit the creation of such semantic user profiles are the identification of user's long-term interests and the adaptation of retrieval results based on these personal interests. Most personalisation services rely on users explicitly specifying preferences, a common approach in the text retrieval domain. By giving explicit feedback, users are forced to update their need, which can be problematic when their information need is vague. Furthermore, users tend not to provide enough feedback on which to base an adaptive retrieval algorithm. Deviating from the method of explicitly asking the user to rate the relevance of retrieval results, the use of implicit feedback techniques helps by learning user interests unobtrusively. The main advantage is that users are relieved from providing feedback. A disadvantage is that information gathered using implicit techniques is less accurate than information based on explicit feedback. In this thesis, we focus on three main research questions. First of all, we study whether implicit relevance feedback, which is provided while interacting with a video retrieval system, can be employed to bridge the Semantic Gap. We therefore first identify implicit indicators of relevance by analysing representative video retrieval interfaces. Studying whether these indicators can be exploited as implicit feedback within short retrieval sessions, we recommend video documents based on implicit actions performed by a community of users. Secondly, implicit relevance feedback is studied as potential source to build user profiles and hence to identify users' long-term interests in specific topics. This includes studying the identification of different aspects of interests and storing these interests in dynamic user profiles. Finally, we study how this feedback can be exploited to adapt retrieval results or to recommend related videos that match the users' interests. We analyse our research questions by performing both simulation-based and user-centred evaluation studies. The results suggest that implicit relevance feedback can be employed in the video domain and that semantic-based user profiles have the potential to improve video exploration

Multi-modal surrogates for retrieving and making sense of videos: is synchronization between the multiple modalities optimal?

Video surrogates can help people quickly make sense of the content of a video before downloading or seeking more detailed information. Visual and audio features of a video are primary information carriers and might become important components of video retrieval and video sense-making. In the past decades, most research and development efforts on video surrogates have focused on visual features of the video, and comparatively little work has been done on audio surrogates and examining their pros and cons in aiding users' retrieval and sense-making of digital videos. Even less work has been done on multi-modal surrogates, where more than one modality are employed for consuming the surrogates, for example, the audio and visual modalities. This research examined the effectiveness of a number of multi-modal surrogates, and investigated whether synchronization between the audio and visual channels is optimal. A user study was conducted to evaluate six different surrogates on a set of six recognition and inference tasks to answer two main research questions: (1) How do automatically-generated multi-modal surrogates compare to manually-generated ones in video retrieval and video sense-making? and (2) Does synchronization between multiple surrogate channels enhance or inhibit video retrieval and video sense-making? Forty-eight participants participated in the study, in which the surrogates were measured on the the time participants spent on experiencing the surrogates, the time participants spent on doing the tasks, participants' performance accuracy on the tasks, participants' confidence in their task responses, and participants' subjective ratings on the surrogates. On average, the uncoordinated surrogates were more helpful than the coordinated ones, but the manually-generated surrogates were only more helpful than the automatically-generated ones in terms of task completion time. Participants' subjective ratings were more favorable for the coordinated surrogate C2 (Magic A + V) and the uncoordinated surrogate U1 (Magic A + Storyboard V) with respect to usefulness, usability, enjoyment, and engagement. The post-session questionnaire comments demonstrated participants' preference for the coordinated surrogates, but the comments also revealed the value of having uncoordinated sensory channels

Strategies for image visualisation and browsing

PhDThe exploration of large information spaces has remained a challenging task even though the proliferation of database management systems and the state-of-the art retrieval algorithms is becoming pervasive. Signi cant research attention in the multimedia domain is focused on nding automatic algorithms for organising digital image collections into meaningful structures and providing high-semantic image indices. On the other hand, utilisation of graphical and interactive methods from information visualisation domain, provide promising direction for creating e cient user-oriented systems for image management. Methods such as exploratory browsing and query, as well as intuitive visual overviews of image collection, can assist the users in nding patterns and developing the understanding of structures and content in complex image data-sets. The focus of the thesis is combining the features of automatic data processing algorithms with information visualisation. The rst part of this thesis focuses on the layout method for displaying the collection of images indexed by low-level visual descriptors. The proposed solution generates graphical overview of the data-set as a combination of similarity based visualisation and random layout approach. Second part of the thesis deals with problem of visualisation and exploration for hierarchical organisation of images. Due to the absence of the semantic information, images are considered the only source of high-level information. The content preview and display of hierarchical structure are combined in order to support image retrieval. In addition to this, novel exploration and navigation methods are proposed to enable the user to nd the way through database structure and retrieve the content. On the other hand, semantic information is available in cases where automatic or semi-automatic image classi ers are employed. The automatic annotation of image items provides what is referred to as higher-level information. This type of information is a cornerstone of multi-concept visualisation framework which is developed as a third part of this thesis. This solution enables dynamic generation of user-queries by combining semantic concepts, supported by content overview and information ltering. Comparative analysis and user tests, performed for the evaluation of the proposed solutions, focus on the ways information visualisation a ects the image content exploration and retrieval; how e cient and comfortable are the users when using di erent interaction methods and the ways users seek for information through di erent types of database organisation