Human-machine cooperation in large-scale multimedia retrieval : a survey

Large-Scale Multimedia Retrieval(LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more interdisciplinary approach is necessary to develop an LSMR system that is really meaningful for humans. To this end, this paper aims to stimulate attention to the LSMR problem from diverse research fields. By explaining basic terminologies in LSMR, we first survey several representative methods in chronological order. This reveals that due to prioritizing the generality and scalability for large-scale data, recent methods interpret semantic meanings with a completely different mechanism from humans, though such humanlike mechanisms were used in classical heuristic-based methods. Based on this, we discuss human-machine cooperation, which incorporates knowledge about human interpretation into LSMR without sacrificing the generality and scalability. In particular, we present three approaches to human-machine cooperation (cognitive, ontological, and adaptive), which are attributed to cognitive science, ontology engineering, and metacognition, respectively. We hope that this paper will create a bridge to enable researchers in different fields to communicate about the LSMR problem and lead to a ground-breaking next generation of LSMR systems

Human-Machine Cooperation in Large-Scale Multimedia Retrieval: A Survey

Large-Scale Multimedia Retrieval(LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more interdisciplinary approach is necessary to develop an LSMR system that is really meaningful for humans. To this end, this paper aims to stimulate attention to the LSMR problem from diverse research fields. By explaining basic terminologies in LSMR, we first survey several representative methods in chronological order. This reveals that due to prioritizing the generality and scalability for large-scale data, recent methods interpret semantic meanings with a completely different mechanism from humans, though such humanlike mechanisms were used in classical heuristic-based methods. Based on this, we discuss human-machine cooperation, which incorporates knowledge about human interpretation into LSMR without sacrificing the generality and scalability. In particular, we present three approaches to human-machine cooperation (cognitive, ontological, and adaptive), which are attributed to cognitive science, ontology engineering, and metacognition, respectively. We hope that this paper will create a bridge to enable researchers in different fields to communicate about the LSMR problem and lead to a ground-breaking next generation of LSMR systems

Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.Comment: to appear in ACM Computing Survey

Visual approaches to knowledge organization and contextual exploration

This thesis explores possible visual approaches for the representation of semantic structures, such as zz-structures. Some holistic visual representations of complex domains have been investigated through the proposal of new views - the so-called zz-views - that allow both to make visible the interconnections between elements and to support a contextual and multilevel exploration of knowledge. The potential of this approach has been examined in the context of two case studies that have led to the creation of two Web applications. The \ufb01rst domain of study regarded the visual representation, analysis and management of scienti\ufb01c bibliographies. In this context, we modeled a Web application, we called VisualBib, to support researchers in building, re\ufb01ning, analyzing and sharing bibliographies. We adopted a multi-faceted approach integrating features that are typical of three di\ufb00erent classes of tools: bibliography visual analysis systems, bibliographic citation indexes and personal research assistants. The evaluation studies carried out on a \ufb01rst prototype highlighted the positive impact of our visual model and encouraged us to improve it and develop further visual analysis features we incorporated in the version 3.0 of the application. The second case study concerned the modeling and development of a multimedia catalog of Web and mobile applications. The objective was to provide an overview of a significant number of tools that can help teachers in the implementation of active learning approaches supported by technology and in the design of Teaching and Learning Activities (TLAs). We analyzed and documented 281 applications, preparing for each of them a detailed multilingual card and a video-presentation, organizing all the material in an original purpose-based taxonomy, visually represented through a browsable holistic view. The catalog, we called AppInventory, provides contextual exploration mechanisms based on zz-structures, collects user contributions and evaluations about the apps and o\ufb00ers visual analysis tools for the comparison of the applications data and user evaluations. The results of two user studies carried out on groups of teachers and students shown a very positive impact of our proposal in term of graphical layout, semantic structure, navigation mechanisms and usability, also in comparison with two similar catalogs

Semantic interpretation of events in lifelogging

The topic of this thesis is lifelogging, the automatic, passive recording of a person’s daily activities and in particular, on performing a semantic analysis and enrichment of lifelogged data. Our work centers on visual lifelogged data, such as taken from wearable cameras. Such wearable cameras generate an archive of a person’s day taken from a first-person viewpoint but one of the problems with this is the sheer volume of information that can be generated. In order to make this potentially very large volume of information more manageable, our analysis of this data is based on segmenting each day’s lifelog data into discrete and non-overlapping events corresponding to activities in the wearer’s day. To manage lifelog data at an event level, we define a set of concepts using an ontology which is appropriate to the wearer, applying automatic detection of concepts to these events and then semantically enriching each of the detected lifelog events making them an index into the events. Once this enrichment is complete we can use the lifelog to support semantic search for everyday media management, as a memory aid, or as part of medical analysis on the activities of daily living (ADL), and so on. In the thesis, we address the problem of how to select the concepts to be used for indexing events and we propose a semantic, density- based algorithm to cope with concept selection issues for lifelogging. We then apply activity detection to classify everyday activities by employing the selected concepts as high-level semantic features. Finally, the activity is modeled by multi-context representations and enriched by Semantic Web technologies. The thesis includes an experimental evaluation using real data from users and shows the performance of our algorithms in capturing the semantics of everyday concepts and their efficacy in activity recognition and semantic enrichment