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Video summarisation is one of the most active fields in content‐based video retrieval research. A new video summarisation scheme is proposed by this paper based on socially generated temporal tags.

To capture users' collaborative tagging activities the proposed scheme maintains video bookmarks, which contain some temporal or positional information about videos, such as relative time codes or byte offsets. For each video all the video bookmarks collected from users are then statistically analysed in order to extract some meaningful key frames (the video equivalent of keywords), which collectively constitute the summary of the video.

Compared with traditional video summarisation methods that use low‐level audio‐visual features, the proposed method is based on users' high‐level collaborative activities, and thus can produce semantically more important summaries than existing methods.

It is assumed that the video frames around the bookmarks inserted by users are informative and representative, and therefore can be used as good sources for summarising videos.

Folksonomy, commonly called collaborative tagging, is a Web 2.0 method for users to freely annotate shared information resources with keywords. It has mostly been used for collaboratively tagging photos (Flickr), web site bookmarks (Del.icio.us), or blog posts (Technorati), but has never been applied to the field of automatic video summarisation. It is believed that this is the first attempt to utilise users' high‐level collaborative tagging activities, instead of low‐level audio‐visual features, for video summarisation.

Affordable mobile devices with video playback functionality are rapidly growing in the market. Current wireless and third generation communication networks enable smoother and higher quality streaming video. With the support of these technologies, most participants in telecom value‐added service chain are planning to shift their business focus to a more profitable and appealing area, mobile TV. Previous work that survey on users' behavior while consuming mobile TV has indicated that users normally watch brief and casual contents, and not the full program. However, most of the current services adopt a “push” approach since users passively receive pre‐defined contents, rather than pulling the interesting topics and segments. In order to promote a more enjoyable and rewarding watching experience, this paper will propose a framework to support a fully interactive mobile TV. The main goal is to enable users to: 1) visually locate interesting topics across multiple genres (such as news, sports and entertainment) and 2) fully control the playback flow of the multimedia items while selecting the most interesting segments. A web‐based system has been developed to implement and test the effectiveness of the proposed framework in a wireless and mobile setting.

Keywords search is intuitive, simple to use and convenient. It is also the de facto input interface for textual and multimedia retrieval. However, individuals often perform poorly when faced with exploratory search tasks that are common during learning, resulting in poor quality searches. The purpose of this paper is to examine how adolescent learners search and select videos to support self-learning. The findings allow for the identification of design concepts of video retrieval interface and features that can facilitate better exploratory searches.

Participants were assigned two customized video search tasks. The think-aloud protocol is used to allow participants to verbalize their actions, thoughts and feeling. This approach offered rich insights to the participants’ cognitive processes and considerations when performing the search tasks.

This study identified five themes for exploratory video search behavior: selection of internet resources, query formulation/reformulation, selection of the video(s) for preview, getting acquainted with the video content, and making a decision for the search task. The analysis of these themes led to a number of design concepts, ranging from supporting exploration of topics to better interaction with metadata.

The findings can inform future development of dedicated video retrieval systems interfaces that seeks to facilitate effective exploratory searches by learners.

This study contributes by suggesting design concepts for video retrieval system developers to support exploratory video searches.

The space-time variants algorithm will not give good results in practical scenarios; when no tubes increase, these techniques will not give the results. It is challenging to reduce the energy of the output synopsis videos. In this paper, a new optimized technique has been implemented that models and covers every frame in the output video.

In the video synopsis, condensing a video to produce a low frame rate (FR) video using their spatial and temporal coefficients is vital in complex environments. Maintaining a database is also feasible and consumes space. In recent years, many algorithms were proposed.

The main advantage of this proposed technique is that the output frames are selected by the user definitions and stored in low-intensity communication systems and also it gives tremendous support to the user to select desired tubes and thereby stops the criterion in the output video, which can be further suitable for the user's knowledge and creates nonoverlapping tube-oriented synopsis that can provide excellent visual experience.

In this research paper, four test videos are utilized with complex environments (high-density objects) and show that the proposed technique gives better results when compared to other existing techniques.

The proposed method provides a unique technique in video synopsis for compressing the data without loss.

The need of tools for content analysis, information extraction and retrieval of multimedia objects in their native form is strongly emphasized into the judicial domain: digital videos represent a fundamental informative source of events occurring during judicial proceedings that should be stored, organized and retrieved in short time and with low cost. This paper seeks to address these issues.

In this context the JUMAS system, stem from the homonymous European Project (www.jumasproject.eu), takes up the challenge of exploiting semantics and machine learning techniques towards a better usability of multimedia judicial folders.

In this paper one of the most challenging issues addressed by the JUMAS project is described: extracting meaningful abstracts of given judicial debates in order to efficiently access salient contents. In particular, the authors present an ontology enhanced multimedia summarization environment able to derive a synthetic representation of judicial media contents by a limited loss of meaningful information while overcoming the information overload problem.

The adoption of ontology‐based query expansion has made it possible to improve the performance of multimedia summarization algorithms with respect to the traditional approaches based on statistics. The effectiveness of the proposed approach has been evaluated on real media contents, highlighting a good potential for extracting key events in the challenging area of judicial proceedings.

This paper aims to build on the latest advances in cognitive computing techniques to systematically illustrate how unstructured data from users can offer significant value for co-innovation.

The paper adopts a general overview approach to understand how unstructured data from users can be analyzed with cognitive computing techniques for innovation. The paper links the computerized techniques with marketing innovation problems with an integrated framework using dynamic capabilities and complexity theory.

The paper identifies a suite of methodologies for facilitating company co-innovation via engaging with customers and external data with cognitive computing technologies. It helps to expand marketing researchers and practitioners’ understanding of using unstructured data.

This paper provides a conceptual framework that divides co-innovation process into three stages, ideas generation, ideas integration and ideas evaluation, and maps cognitive computing methodologies and technologies to each stage. This paper makes the theoretical contributions by developing propositions from both customer and firm perspectives.

This paper can be used for companies to engage consumers and external data for co-innovation activities by strategically select appropriate cognitive computing techniques to analyze unstructured data for better insights.

Given the lack of systematic discussion regarding what is possible from using cognitive computing to analyze unstructured data for co-innovation. This paper makes first attempt to summarize how unstructured data can be analyzed with cognitive computing techniques. This paper also integrates complexity theory to the framework from a novel perspective.

The growth of police operated surveillance cameras has out-paced the ability of humans to monitor them effectively. Computer vision is a possible solution. An ongoing research project on the application of computer vision within a municipal police department is described. The paper aims to discuss these issues.

Following the demystification of computer vision technology, its potential for police agencies is developed within a focus on computer vision as a solution for two common surveillance camera tasks (live monitoring of multiple surveillance cameras and summarizing archived video files). Three unaddressed research questions (can specialized computer vision applications for law enforcement be developed at this time, how will computer vision be utilized within existing public safety camera monitoring rooms, and what are the system-wide impacts of a computer vision capability on local criminal justice systems) are considered.

Despite computer vision becoming accessible to law enforcement agencies the impact of computer vision has not been discussed or adequately researched. There is little knowledge of computer vision or its potential in the field.

This paper introduces and discusses computer vision from a law enforcement perspective and will be valuable to police personnel tasked with monitoring large camera networks and considering computer vision as a system upgrade.

A fine‐grained video content indexing, retrieval, and adaptation requires accurate metadata describing the video structure and semantics to the lowest granularity, i.e. to the object level. The authors address these requirements by proposing semantic video content annotation tool (SVCAT) for structural and high‐level semantic video annotation. SVCAT is a semi‐automatic MPEG‐7 standard compliant annotation tool, which produces metadata according to a new object‐based video content model introduced in this work. Videos are temporally segmented into shots and shots level concepts are detected automatically using ImageNet as background knowledge. These concepts are used as a guide to easily locate and select objects of interest which are then tracked automatically to generate an object level metadata. The integration of shot based concept detection with object localization and tracking drastically alleviates the task of an annotator. The paper aims to discuss these issues.

A systematic keyframes classification into ImageNet categories is used as the basis for automatic concept detection in temporal units. This is then followed by an object tracking algorithm to get exact spatial information about objects.

Experimental results showed that SVCAT is able to provide accurate object level video metadata.

The new contribution in this paper introduces an approach of using ImageNet to get shot level annotations automatically. This approach assists video annotators significantly by minimizing the effort required to locate salient objects in the video.

The purpose of this paper is to present a framework applicable to interactive video retrieval. The objective of the framework is so that it can be applied conceptually for understanding users and use of video digital libraries, and also practically for designing retrieval components like user interfaces.

The framework was developed through a user-centered and analytical approach, and serves as an initial attempt at generalizing how users interact when searching and browsing digital video, throughout different situations, along with the general designs that can be supportive.

The framework is two-fold, yet, together, comprises one set of conceptual findings. The first component of the framework depicts generalized user interactions throughout varying contexts of an interactive video retrieval process, followed by a second component, an illustration of the resulting supportive interface designs or sets of features. Cautions from previous studies not to over generalize the interactive process were heeded.

The implications for such research are based on the understanding that video retrieval will benefit from the advancement of user-centered foundations, which can guide and support design decisions for resources like digital libraries.

The need for this study is rather straightforward: there is currently not enough conceptual research of interactive video retrieval from a user-centered perspective, which contrasts with other areas of information retrieval research where the interaction process has been thoroughly examined for a variety of domains and contexts with implications for different retrieval tools like OPACs, search engines, and article databases.